From 570eed60fc3b1658f97a7a931233e6d9aacd48f6 Mon Sep 17 00:00:00 2001 From: "@daaronr" <8229168+daaronr@users.noreply.github.com> Date: Thu, 20 Jul 2023 08:25:12 -0400 Subject: [PATCH] small fixes, moving sections around --- .../evaluation_data/execute-results/html.json | 6 +- .../figure-html/unnamed-chunk-14-1.png | Bin 66287 -> 61347 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 163778 -> 25763 bytes .../figure-html/unnamed-chunk-18-1.png | Bin 67808 -> 102021 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 102021 -> 25763 bytes chapters/evaluation_data.qmd | 191 +- data/evals.Rdata | Bin 3354 -> 3265 bytes data/evals.csv | 20 +- docs/chapters/evaluation_data.html | 1989 +++++++++-------- .../figure-html/unnamed-chunk-14-1.png | Bin 66287 -> 61347 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 163778 -> 25763 bytes .../figure-html/unnamed-chunk-18-1.png | Bin 67808 -> 102021 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 102021 -> 25763 bytes docs/search.json | 8 +- 14 files changed, 1163 insertions(+), 1051 deletions(-) diff --git a/_freeze/chapters/evaluation_data/execute-results/html.json b/_freeze/chapters/evaluation_data/execute-results/html.json index b8ee42a..c0e3a07 100644 --- a/_freeze/chapters/evaluation_data/execute-results/html.json +++ b/_freeze/chapters/evaluation_data/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "104e91080201a5b92c7aa752f7089242", + "hash": "56f73f1b5509f9113084b441c3554330", "result": { - "markdown": "# Evaluation data: description, exploration, checks\n\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"load packages\"}\nsource(here::here(\"code\", \"shared_packages_code.R\"))\n\n#devtools::install_github(\"rethinkpriorities/rp-r-package\")\nlibrary(rethinkpriorities)\n\n#devtools::install_github(\"rethinkpriorities/r-noodling-package\") #mainly used playing in real time\nlibrary(rnoodling)\n\nlibrary(here)\nlibrary(dplyr)\nlibrary(pacman)\n\np_load(formattable, sparkline, install=FALSE)\n\np_load(DT, santoku, lme4, huxtable, janitor, emmeans, sjPlot, sjmisc, ggeffects, ggrepel, likert, labelled, plotly, stringr, install=FALSE)\n\np_load(ggthemes, paletteer, ggridges, install=FALSE)\n\nselect <- dplyr::select \n\noptions(knitr.duplicate.label = \"allow\")\n\noptions(mc.cores = parallel::detectCores())\n#rstan_options(auto_write = TRUE)\n\n#library(hunspell)\n\n#(brms)\n\n#devtools::install_github(\"bergant/airtabler\")\np_load(airtabler)\n\n#remotes::install_github(\"rmcelreath/rethinking\")\n#library(rethinking)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"install aggrecat package\"}\n#devtools::install_github(\"metamelb-repliCATS/aggreCAT\")\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"input from airtable\"}\nbase_id <- \"appbPYEw9nURln7Qg\"\n\n# Set your Airtable API key\n#Sys.setenv(AIRTABLE_API_KEY = \"\") \n#this should be set in my .Renviron file\n\n\n# Read data from a specific view\n\nevals <- air_get(base = base_id, \"output_eval\") \n\n\nall_pub_records <- data.frame()\npub_records <- air_select(base = base_id, table = \"crucial_research\")\n\n# Append the records to the list\nall_pub_records <- bind_rows(all_pub_records, pub_records)\n\n# While the length of the records list is 100 (the maximum), fetch more records\nwhile(nrow(pub_records) == 100) {\n # Get the ID of the last record in the list\n offset <- get_offset(pub_records)\n \n # Fetch the next 100 records, starting after the last ID\n pub_records <- air_select(base = base_id, table = \"crucial_research\", offset = offset)\n \n # Append the records to the df\n all_pub_records <- bind_rows(all_pub_records, pub_records)\n}\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"just the useful and publish-able data, clean a bit\"}\ncolnames(evals) <- snakecase::to_snake_case(colnames(evals))\n\nevals_pub <- evals %>% \n dplyr::rename(stage_of_process = stage_of_process_todo_from_crucial_research_2) %>% \n mutate(stage_of_process = unlist(stage_of_process)) %>% \n dplyr::filter(stage_of_process == \"published\") %>% \n select(id, crucial_research, evaluator_name, category, source_main, author_agreement, overall, lb_overall, ub_overall, conf_index_overall, advancing_knowledge_and_practice, lb_advancing_knowledge_and_practice, ub_advancing_knowledge_and_practice, conf_index_advancing_knowledge_and_practice, methods_justification_reasonableness_validity_robustness, lb_methods_justification_reasonableness_validity_robustness, ub_methods_justification_reasonableness_validity_robustness, conf_index_methods_justification_reasonableness_validity_robustness, logic_communication, lb_logic_communication, ub_logic_communication, conf_index_logic_communication, engaging_with_real_world_impact_quantification_practice_realism_and_relevance, lb_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, ub_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, conf_index_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, relevance_to_global_priorities, lb_relevance_to_global_priorities, ub_relevance_to_global_priorities, conf_index_relevance_to_global_priorities, journal_quality_predict, lb_journal_quality_predict, ub_journal_quality_predict, conf_index_journal_quality_predict, open_collaborative_replicable, conf_index_open_collaborative_replicable, lb_open_collaborative_replicable, ub_open_collaborative_replicable, merits_journal, lb_merits_journal, ub_merits_journal, conf_index_merits_journal)\n\nevals_pub %<>%\nmutate(across(everything(), ~ map(.x, ~ ifelse(is.null(.x), NA, .x)), .names = \"{.col}_unlisted\")) %>% # for each co\n tidyr::unnest_wider(category, names_sep = \"\") %>%\nmutate(across(everything(), unlist)) #unlist list columns\n\n\n#Todo -- check the unlist is not propagating the entry\n\n#Note: category, topic_subfield, and source have multiple meaningful categories. These will need care \n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"Shorten names \"}\nnew_names <- c(\n \"eval_name\" = \"evaluator_name\",\n \"cat_1\" = \"category1\",\n \"cat_2\" = \"category2\",\n \"cat_3\" = \"category3\",\n \"crucial_rsx\" = \"crucial_research\",\n \"conf_overall\" = \"conf_index_overall\",\n \"adv_knowledge\" = \"advancing_knowledge_and_practice\",\n \"lb_adv_knowledge\" = \"lb_advancing_knowledge_and_practice\",\n \"ub_adv_knowledge\" = \"ub_advancing_knowledge_and_practice\",\n \"conf_adv_knowledge\" = \"conf_index_advancing_knowledge_and_practice\",\n \"methods\" = \"methods_justification_reasonableness_validity_robustness\",\n \"lb_methods\" = \"lb_methods_justification_reasonableness_validity_robustness\",\n \"ub_methods\" = \"ub_methods_justification_reasonableness_validity_robustness\",\n \"conf_methods\" = \"conf_index_methods_justification_reasonableness_validity_robustness\",\n \"logic_comms\" = \"logic_communication\",\n \"lb_logic_comms\" = \"lb_logic_communication\",\n \"ub_logic_comms\" = \"ub_logic_communication\",\n \"conf_logic_comms\" = \"conf_index_logic_communication\",\n \"real_world\" = \"engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"lb_real_world\" = \"lb_engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"ub_real_world\" = \"ub_engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"conf_real_world\" = \"conf_index_engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"gp_relevance\" = \"relevance_to_global_priorities\",\n \"lb_gp_relevance\" = \"lb_relevance_to_global_priorities\",\n \"ub_gp_relevance\" = \"ub_relevance_to_global_priorities\",\n \"conf_gp_relevance\" = \"conf_index_relevance_to_global_priorities\",\n \"journal_predict\" = \"journal_quality_predict\",\n \"lb_journal_predict\" = \"lb_journal_quality_predict\",\n \"ub_journal_predict\" = \"ub_journal_quality_predict\",\n \"conf_journal_predict\" = \"conf_index_journal_quality_predict\",\n \"open_sci\" = \"open_collaborative_replicable\",\n \"conf_open_sci\" = \"conf_index_open_collaborative_replicable\",\n \"lb_open_sci\" = \"lb_open_collaborative_replicable\",\n \"ub_open_sci\" = \"ub_open_collaborative_replicable\",\n \"conf_merits_journal\" = \"conf_index_merits_journal\"\n)\n\nevals_pub <- evals_pub %>%\n rename(!!!new_names)\n\n# make the old names into labels\n\nlibrary(stringr)\n \n# Create a list of labels\nlabels <- str_replace_all(new_names, \"_\", \" \")\nlabels <- str_to_title(labels)\n \n# Assign labels to the dataframe\n# for(i in seq_along(labels)) {\n# col_name <- new_names[names(new_names)[i]]\n# label <- labels[i]\n# attr(evals_pub[[col_name]], \"label\") <- label\n# }\n# \n```\n:::\n\n\n\n\n### Reconcile the uncertainty ratings and CIs (first-pass) {-}\n\nImpute CIs from stated confidence level 'dots', correspondence loosely described [here](https://effective-giving-marketing.gitbook.io/unjournal-x-ea-and-global-priorities-research/policies-projects-evaluation-workflow/evaluation/guidelines-for-evaluators#1-5-dots-explanation-and-relation-to-cis)\n\n::: {.callout-note collapse=\"true\"}\n## Dots to interval choices\n\n> 5 = Extremely confident, i.e., 90% confidence interval spans +/- 4 points or less)\n\nFor 0-100 ratings, code the LB as $min(R - 4\\times \\frac{R}{100},0)$ and the UB as $max(R + 4\\times \\frac{R}{100},0)$, where R is the stated (middle) rating. This 'scales' the CI, as interpreted, to be proportional to the rating, with a maximum 'interval' of about 8, with the rating is about 96.\n\n> 4 = Very*confident: 90% confidence interval +/- 8 points or less\n\nFor 0-100 ratings, code the LB as $min(R - 8\\times \\frac{R}{100},0)$ and the UB as $max(R + 8\\times \\frac{R}{100},0)$, where R is the stated (middle) rating. \n\n> 3 = Somewhat** confident: 90% confidence interval +/- 15 points or less \n\n> 2 = Not very** confident: 90% confidence interval, +/- 25 points or less\n\nComparable scaling for the 2-3 ratings as for the 4 and 5 rating.\n\n> 1 = Not** confident: (90% confidence interval +/- more than 25 points)\n \nCode LB as $min(R - 37.5\\times \\frac{R}{100},0)$ and the UB as $max(R + 37.5\\times \\frac{R}{100},0)$. \n \nThis is just a first-pass. There might be a more information-theoretic way of doing this. On the other hand, we might be switching the evaluations to use a different tool soon, perhaps getting rid of the 1-5 confidence ratings.\n\n::: \n\n\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"reconcile explicit bounds and stated confidence level\"}\n# Define the baseline widths for each confidence rating\nbaseline_widths <- c(4, 8, 15, 25, 37.5)\n\n# Define a function to calculate the lower and upper bounds, where given only an index\ncalc_bounds <- function(rating, confidence, lb_explicit, ub_explicit, scale=100) {\n # Check if confidence is NA\n if (is.na(confidence)) {\n return(c(lb_explicit, ub_explicit)) # Return explicit bounds if confidence is NA\n } else {\n baseline_width <- baseline_widths[confidence]\n lb <- pmax(rating - baseline_width * rating / scale, 0)\n ub <- pmin(rating + baseline_width * rating / scale, scale)\n return(c(lb, ub))\n }\n}\n\n# Function to calculate bounds for a single category\ncalc_category_bounds <- function(df, category, scale=100) {\n # Calculate bounds\n bounds <- mapply(calc_bounds, df[[category]], df[[paste0(\"conf_\", category)]], df[[paste0(\"lb_\", category)]], df[[paste0(\"ub_\", category)]])\n \n # Convert to data frame and ensure it has the same number of rows as the input\n bounds_df <- as.data.frame(t(bounds))\n rownames(bounds_df) <- NULL\n \n # Add bounds to original data frame\n df[[paste0(category, \"_lb_imp\")]] <- bounds_df[, 1]\n df[[paste0(category, \"_ub_imp\")]] <- bounds_df[, 2]\n \n return(df)\n}\n\n\n# Lists of categories\n\nrating_cats <- c(\"overall\", \"adv_knowledge\", \"methods\", \"logic_comms\", \"real_world\", \"gp_relevance\", \"open_sci\")\n\n#... 'predictions' are currently 1-5 (0-5?)\npred_cats <- c(\"journal_predict\", \"merits_journal\")\n\n# Apply the function to each category\n# DR: I don't love this looping 'edit in place' code approach, but whatever\nfor (cat in rating_cats) {\n evals_pub <- calc_category_bounds(evals_pub, cat, scale=100)\n}\n\nfor (cat in pred_cats) {\n evals_pub <- calc_category_bounds(evals_pub, cat, scale=5)\n}\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"save data for others' use\"}\nevals_pub %>% saveRDS(file = here(\"data\", \"evals.Rdata\"))\nevals_pub %>% write_csv(file = here(\"data\", \"evals.csv\"))\n\n#evals_pub %>% readRDS(file = here(\"data\", \"evals.Rdata\"))\n```\n:::\n\n\n \n# Basic presentation\n\n## Simple data summaries/codebooks/dashboards and visualization\n\nBelow, we give a data table of key attributes of the paper, the author, and the 'middle' ratings and predictions. \n\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"Data datable (all shareable relevant data)\"}\n(\n all_evals_dt <- evals_pub %>%\n arrange(crucial_rsx, eval_name) %>%\n dplyr::select(crucial_rsx, eval_name, everything())) %>%\n dplyr::select(-id) %>% \n dplyr::select(-matches(\"ub_|lb_|conf\")) %>% \n #rename_all(~ gsub(\"_\", \" \", .)) %>% \n rename(\"Research _____________________\" = \"crucial_rsx\" \n ) %>%\n DT::datatable(\n caption = \"Evaluations (confidence bounds not shown)\", \n filter = 'top',\n rownames= FALSE,\n options = list(pageLength = 7)\n )\n```\n:::\n\n\nNext, we present the ratings and predictions along with 'uncertainty measures'. We use \"ub imp\" (and \"lb imp\") to denote the upper and lower bounds given by evaluators. Where evaluators gave only a 1-5 confidence level^[More or less, the ones who report a level for 'conf overall', although some people did this for some but not others], we use the imputations discussed and coded above. \n\n\n::: {.cell}\n\n```{.r .cell-code}\n(\n all_evals_dt_ci <- evals_pub %>%\n arrange(crucial_rsx, eval_name) %>%\n dplyr::select(crucial_rsx, eval_name, conf_overall, matches(\"ub_imp|lb_imp\")) %>%\n #rename_all(~ gsub(\"_\", \" \", .)) %>% \n rename(\"Research _____________________\" = \"crucial_rsx\" \n ) %>%\n DT::datatable(\n caption = \"Evaluations and (imputed*) confidence bounds)\", \n filter = 'top',\n rownames= FALSE,\n options = list(pageLength = 7)\n )\n)\n```\n:::\n\n\n\n- Composition of research evaluated\n - By field (economics, psychology, etc.)\n - By subfield of economics \n - By topic/cause area (Global health, economic development, impact of technology, global catastrophic risks, etc. )\n - By source (submitted, identified with author permission, direct evaluation)\n \n- Timing of intake and evaluation (Consider: timing might be its own section or chapter; this is a major thing journals track, and we want to keep track of ourselves)\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n#Add in the 3 different evaluation input sources\n#update to be automated rather than hard-coded - to look at David's work here\npapers_considered = all_pub_records %>%nrow()\npapers_deprio = all_pub_records %>% filter(`stage of process/todo` == \"de-prioritized\") %>%nrow()\npapers_evaluated = all_pub_records %>% filter(`stage of process/todo` %in% c(\"published\",\n \"contacting/awaiting_authors_response_to_evaluation\",\n \"awaiting_publication_ME_comments\",\n \"awaiting_evaluations\")) %>%nrow()\npapers_complete = all_pub_records %>% filter(`stage of process/todo` == \"published\") %>% nrow()\npapers_in_progress = papers_evaluated-papers_complete\npapers_still_in_consideration = all_pub_records %>% filter(`stage of process/todo` == \"considering\") %>%nrow()\n\n\nfig <- plot_ly(\n type = \"sankey\",\n orientation = \"h\",\n\n node = list(\n label = c(\"All paper considered\", \"Papers evaluated\", \"Papers complete\", \"Papers in progress\", \"Papers still in consideration\", \"Papers rejected\"),\n color = c(\"orange\", \"green\", \"green\", \"orange\", \"orange\", \"red\"),\n pad = 15,\n thickness = 20,\n line = list(\n color = \"black\",\n width = 0.5\n )\n ),\n\n link = list(\n source = c(0,1,1,0,0),\n target = c(1,2,3,4,5),\n value = c(\n papers_evaluated,\n papers_complete,\n papers_in_progress,\n papers_still_in_consideration,\n papers_deprio\n ))\n )\nfig <- fig %>% layout(\n title = \"Unjournal paper funnel\",\n font = list(\n size = 10\n )\n)\n\nfig \n```\n\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n\n### The distribution of ratings and predictions {-}\n\n- For each category and prediction (overall and by paper)\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary_df <- evals_pub %>%\n distinct(crucial_research_unlisted, .keep_all = T) %>% \n group_by(category_unlisted) %>%\n summarise(count = n()) \n\nsummary_df$category_unlisted[is.na(summary_df$category_unlisted)] <- \"Unknown\"\n\nsummary_df <- summary_df %>%\n arrange(-desc(count)) %>%\n mutate(category_unlisted = factor(category_unlisted, levels = unique(category_unlisted)))\n\n# Create stacked bar chart\nggplot(summary_df, aes(x = category_unlisted, y = count)) +\n geom_bar(stat = \"identity\") + \n coord_flip() + # This makes the chart horizontal\n theme_minimal() +\n labs(x = \"Paper category\", y = \"Count\", \n title = \"Count of evaluated papers by category\") \n```\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-14-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nwrap_text <- function(text, width) {\n sapply(strwrap(text, width = width, simplify = FALSE), paste, collapse = \"\\n\")\n}\n\nevals_pub$wrapped_pub_names <- wrap_text(evals_pub$crucial_research_unlisted, width = 60)\n\n\n# original names\noriginal_names <- evals_pub$crucial_research_unlisted\n\n# shortened names\nshortened_names <- c(\"Resilient Foods vs AGI Safety\",\n \"Advance Market Commitments\",\n \"Wildlife Trade Demand\",\n \"Advance Market Commitments\",\n \"Economic Prod. & Biodiversity\",\n \"Advance Market Commitments\",\n \"AI and Economic Growth\",\n \"Wildlife Trade Demand\",\n \"Non-Profits Governance & Impact\",\n \"AI and Economic Growth\",\n \"Mental Health Therapy & Human Capital\",\n \"Economic Prod. & Biodiversity\",\n \"Cash Transfers vs Psychotherapy\",\n \"Cash Transfers vs Psychotherapy\",\n \"Resilient Foods vs AGI Safety\",\n \"Mental Health Therapy & Human Capital\",\n \"Resilient Foods vs AGI Safety\")\n\n# create a named vector for easy lookup\nname_lookup <- setNames(shortened_names, original_names)\n\n# use the lookup to create the new column\nevals_pub$shortened_names <- name_lookup[evals_pub$crucial_research_unlisted]\n\nevals_pub$wrapped_shortened_names <- wrap_text(evals_pub$shortened_names, width = 15)\n\n#Move this to do this 'cleaning' earlier\nevals_pub$revised_evaluator_name <- ifelse(\n grepl(\"^\\\\b\\\\w+\\\\b$|\\\\bAnonymous\\\\b\", evals_pub$evaluator_name_unlisted),\n paste0(\"Anonymous_\", seq_along(evals_pub$evaluator_name_unlisted)),\n evals_pub$evaluator_name_unlisted\n)\n\n\n# Dot plot\nggplot(evals_pub, aes(x = shortened_names, y = overall)) +\n geom_point(stat = \"identity\", size = 4, shape = 1, colour = \"lightblue\", stroke = 3) +\n geom_text_repel(aes(label = revised_evaluator_name), \n size = 3, \n box.padding = unit(0.35, \"lines\"),\n point.padding = unit(0.3, \"lines\")) +\n coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)\n theme_light() +\n xlab(\"Paper\") + # remove x-axis label\n ylab(\"Overall score\") + # name y-axis\n ggtitle(\"Overall scores of evaluated papers\") +# add title\n theme(\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n text = element_text(size = 14), # changing all text size to 16\n axis.text.y = element_text(size = 10),\n axis.text.x = element_text(size = 12)\n )\n```\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-16-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# Function to insert a newline character every 15 characters\nwrap_text <- function(x, width = 15) {\n gsub(\"(.{1,15})\", \"\\\\1-\\n\", x)\n}\n\nevals_pub$source_main_wrapped <- wrap_text(evals_pub$source_main, 15)\n\n# Bar plot\nggplot(evals_pub, aes(x = source_main_wrapped)) + \n geom_bar(position = \"stack\", stat = \"count\") +\n labs(x = \"Source\", y = \"Count\") +\n coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)\n theme_light() +\n theme_minimal() +\n ggtitle(\"Evaluations by source of the paper\") + # add title\n theme(\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n text = element_text(size = 16), # changing all text size to 16\n axis.text.y = element_text(size = 10),\n axis.text.x = element_text(size = 14)\n )\n```\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-18-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nall_pub_records$is_evaluated = all_pub_records$`stage of process/todo` %in% c(\"published\",\n \"contacting/awaiting_authors_response_to_evaluation\",\n \"awaiting_publication_ME_comments\",\n \"awaiting_evaluations\")\n\nall_pub_records$source_main[all_pub_records$source_main == \"NA\"] <- \"Not applicable\" \nall_pub_records$source_main[all_pub_records$source_main == \"internal-from-syllabus-agenda-policy-database\"] <- \"Internal: syllabus, agenda, etc.\" \nall_pub_records$source_main = tidyr::replace_na(all_pub_records$source_main, \"Unknown\")\n\n\n\nggplot(all_pub_records, aes(x = fct_infreq(source_main), fill = is_evaluated)) + \n geom_bar(position = \"stack\", stat = \"count\") +\n labs(x = \"Source\", y = \"Count\", fill = \"Selected for\\nevaluation?\") +\n coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)\n theme_light() +\n theme_minimal() +\n ggtitle(\"Evaluations by source of the paper\") +# add title\n theme(\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n text = element_text(size = 16), # changing all text size to 16\n axis.text.y = element_text(size = 12),\n axis.text.x = element_text(size = 14)\n )\n```\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-20-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nunit.scale = function(x) (x*100 - min(x*100)) / (max(x*100) - min(x*100))\nevaluations_table <- evals_pub %>%\n select(crucial_rsx, eval_name, cat_1, source_main, overall, adv_knowledge, methods, logic_comms, journal_predict) %>%\n arrange(desc(crucial_rsx))\n\n\nout = formattable(\n evaluations_table,\n list(\n #area(col = 5:8) ~ function(x) percent(x / 100, digits = 0),\n area(col = 5:8) ~ color_tile(\"#FA614B66\",\"#3E7DCC\"),\n `journal_predict` = proportion_bar(\"#DeF7E9\", unit.scale)\n )\n)\nout\n```\n\n::: {.cell-output-display}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n
crucial_rsx eval_name cat_1 source_main overall adv_knowledge methods logic_comms journal_predict
The Governance Of Non-Profits And Their Social Impact: Evidence From A Randomized Program In Healthcare In DRC Wayne Aaron Sandholtz GH&D internal-NBER 65 70 60 55 3.6
The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being Anonymous Reviewer 1 GH&D internal-NBER 90 90 90 80 4.0
The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being Hannah Metzler GH&D internal-NBER 75 70 90 75 3.0
Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed \"Cognitive Behavioral Therapy among Ghana's Rural Poor Is Effective Regardless of Baseline Mental Distress\") b62275b05d45f43cce4e494d31a07c19 NA internal-NBER 75 60 90 70 4.0
Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed \"Cognitive Behavioral Therapy among Ghana's Rural Poor Is Effective Regardless of Baseline Mental Distress\") 47273de4862aaff608f9086d4d643054 NA internal-NBER 75 65 60 75 NA
Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al) Scott Janzwood long-term-relevant submitted 65 NA NA NA NA
Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al) Anca Hanea long-term-relevant submitted 80 80 70 85 3.5
Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al) Alex Bates long-term-relevant submitted 40 30 50 60 2.0
Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73). David Manheim policy internal-from-syllabus-agenda-policy-database 80 25 95 75 3.0
Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73). Joel Tan policy internal-from-syllabus-agenda-policy-database 79 90 70 70 5.0
Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73). Dan Tortorice policy internal-from-syllabus-agenda-policy-database 80 90 80 80 4.0
Banning wildlife trade can boost demand for unregulated threatened species Anonymous conservation submitted 75 70 80 70 3.0
Banning wildlife trade can boost demand for unregulated threatened species Liew Jia Huan conservation submitted 75 80 50 70 2.5
Aghion, P., Jones, B.F., and Jones, C.I., 2017. Artificial Intelligence and Economic Growth Phil Trammel macroeconomics internal-from-syllabus-agenda-policy-database 92 97 70 45 3.5
Aghion, P., Jones, B.F., and Jones, C.I., 2017. Artificial Intelligence and Economic Growth Seth Benzell macroeconomics internal-from-syllabus-agenda-policy-database 80 75 80 70 NA
\"The Environmental Effects of Economic Production: Evidence from Ecological Observations\n (previous title: Economic Production and Biodiversity in the United States)\" Elias Cisneros NA internal-NBER 88 90 75 80 4.0
\"The Environmental Effects of Economic Production: Evidence from Ecological Observations\n (previous title: Economic Production and Biodiversity in the United States)\" 1ef6aff67012a1750f88f631fddb346c NA internal-NBER 70 70 70 75 4.0
\n:::\n:::\n\n\n\n\n- By field and topic area of paper\n\n- By submission/selection route\n\n- By evaluation manager\n\n\\\n\n\n\n\n### Relationship among the ratings (and predictions) {-} \n\n- Correlation matrix\n\n- ANOVA\n\n- PCA (Principle components)\n\n- With other 'control' factors?\n\n- How do the specific measures predict the aggregate ones (overall rating, merited publication)\n - CF 'our suggested weighting'\n\n\n## Aggregation of expert opinion (modeling)\n\n\n## Notes on sources and approaches\n\n\n::: {.callout-note collapse=\"true\"}\n\n## Hanea et al {-}\n(Consult, e.g., repliCATS/Hanea and others work; meta-science and meta-analysis approaches)\n\n`aggrecat` package\n\n> Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.\n\n[Hanea et al, 2021](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256919#sec007)\n\n However, these are in a different context. Most of those measures are designed to deal with probablistic forecasts for binary outcomes, where the predictor also gives a 'lower bound' and 'upper bound' for that probability. We could roughly compare that to our continuous metrics with 90% CI's (or imputations for these).\n\nFurthermore, many (all their successful measures?) use 'performance-based weights', accessing metrics from prior prediction performance of the same forecasters We do not have these, nor do we have a sensible proxy for this. \n:::\n\n\n::: {.callout-note collapse=\"true\"}\n## D Veen et al (2017)\n\n[link](https://www.researchgate.net/profile/Duco-Veen/publication/319662351_Using_the_Data_Agreement_Criterion_to_Rank_Experts'_Beliefs/links/5b73e2dc299bf14c6da6c663/Using-the-Data-Agreement-Criterion-to-Rank-Experts-Beliefs.pdf)\n\n... we show how experts can be ranked based on their knowledge and their level of (un)certainty. By letting experts specify their knowledge in the form of a probability distribution, we can assess how accurately they can predict new data, and how appropriate their level of (un)certainty is. The expert’s specified probability distribution can be seen as a prior in a Bayesian statistical setting. We evaluate these priors by extending an existing prior-data (dis)agreement measure, the Data Agreement Criterion, and compare this approach to using Bayes factors to assess prior specification. We compare experts with each other and the data to evaluate their appropriateness. Using this method, new research questions can be asked and answered, for instance: Which expert predicts the new data best? Is there agreement between my experts and the data? Which experts’ representation is more valid or useful? Can we reach convergence between expert judgement and data? We provided an empirical example ranking (regional) directors of a large financial institution based on their predictions of turnover. \n\nBe sure to consult the [correction made here](https://www.semanticscholar.org/paper/Correction%3A-Veen%2C-D.%3B-Stoel%2C-D.%3B-Schalken%2C-N.%3B-K.%3B-Veen-Stoel/a2882e0e8606ef876133f25a901771259e7033b1)\n\n::: \n\n\n::: {.callout-note collapse=\"true\"}\n## Also seems relevant:\n\nSee [Gsheet HERE](https://docs.google.com/spreadsheets/d/14japw6eLGpGjEWy1MjHNJXU1skZY_GAIc2uC2HIUalM/edit#gid=0), generated from an Elicit.org inquiry.\n\n\n::: \n\n\n\nIn spite of the caveats in the fold above, we construct some measures of aggregate beliefs using the `aggrecat` package. We will make (and explain) some ad-hoc choices here. We present these:\n\n1. For each paper\n2. For categories of papers and cross-paper categories of evaluations\n3. For the overall set of papers and evaluations\n\nWe can also hold onto these aggregated metrics for later use in modeling.\n\n\n- Simple averaging\n\n- Bayesian approaches \n\n- Best-performing approaches from elsewhere \n\n- Assumptions over unit-level random terms \n\n### Explicit modeling of 'research quality' (for use in prizes, etc.) {-}\n\n- Use the above aggregation as the outcome of interest, or weight towards categories of greater interest?\n\n- Model with controls -- look for greatest positive residual? \n\n\n## Inter-rater reliability\n\n## Decomposing variation, dimension reduction, simple linear models\n\n\n## Later possiblities\n\n- Relation to evaluation text content (NLP?)\n\n- Relation/prediction of later outcomes (traditional publication, citations, replication)\n\n\n\n## Scoping our future coverage\n\nWe have funding to evaluate roughly 50-70 papers/projects per year, given our proposed incentives.\n\nConsider:\n\n- How many relevant NBER papers come out per year?\n\n- How much relevant work in other prestige archives?\n\n- What quotas do we want (by cause, etc.) and how feasible are these?\n\n", + "markdown": "# Evaluation data: description, exploration, checks\n\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"load packages\"}\nsource(here::here(\"code\", \"shared_packages_code.R\"))\n\n#devtools::install_github(\"rethinkpriorities/rp-r-package\")\nlibrary(rethinkpriorities)\n\n#devtools::install_github(\"rethinkpriorities/r-noodling-package\") #mainly used playing in real time\nlibrary(rnoodling)\n\nlibrary(here)\nlibrary(dplyr)\nlibrary(pacman)\n\np_load(formattable, sparkline, install=FALSE)\n\np_load(DT, santoku, lme4, huxtable, janitor, emmeans, sjPlot, sjmisc, ggeffects, ggrepel, likert, labelled, plotly, stringr, install=FALSE)\n\np_load(ggthemes, paletteer, ggridges, install=FALSE)\n\nselect <- dplyr::select \n\noptions(knitr.duplicate.label = \"allow\")\n\noptions(mc.cores = parallel::detectCores())\n#rstan_options(auto_write = TRUE)\n\n#library(hunspell)\n\n#(brms)\n\n#devtools::install_github(\"bergant/airtabler\")\np_load(airtabler)\n\n#remotes::install_github(\"rmcelreath/rethinking\")\n#library(rethinking)\n```\n:::\n\n\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"input from airtable\"}\nbase_id <- \"appbPYEw9nURln7Qg\"\n\n# Set your Airtable API key\n#Sys.setenv(AIRTABLE_API_KEY = \"\") \n#this should be set in my .Renviron file\n\n\n# Read data from a specific view\n\nevals <- air_get(base = base_id, \"output_eval\") \n\nall_pub_records <- data.frame()\npub_records <- air_select(base = base_id, table = \"crucial_research\")\n\n# Append the records to the list\nall_pub_records <- bind_rows(all_pub_records, pub_records)\n\n# While the length of the records list is 100 (the maximum), fetch more records\nwhile(nrow(pub_records) == 100) {\n # Get the ID of the last record in the list\n offset <- get_offset(pub_records)\n \n # Fetch the next 100 records, starting after the last ID\n pub_records <- air_select(base = base_id, table = \"crucial_research\", offset = offset)\n \n # Append the records to the df\n all_pub_records <- bind_rows(all_pub_records, pub_records)\n}\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"just the useful and publish-able data, clean a bit\"}\ncolnames(evals) <- snakecase::to_snake_case(colnames(evals))\n\nevals_pub <- evals %>% \n dplyr::rename(stage_of_process = stage_of_process_todo_from_crucial_research_2) %>% \n mutate(stage_of_process = unlist(stage_of_process)) %>% \n dplyr::filter(stage_of_process == \"published\") %>% \n select(id, crucial_research, paper_abbrev, evaluator_name, category, source_main, author_agreement, overall, lb_overall, ub_overall, conf_index_overall, advancing_knowledge_and_practice, lb_advancing_knowledge_and_practice, ub_advancing_knowledge_and_practice, conf_index_advancing_knowledge_and_practice, methods_justification_reasonableness_validity_robustness, lb_methods_justification_reasonableness_validity_robustness, ub_methods_justification_reasonableness_validity_robustness, conf_index_methods_justification_reasonableness_validity_robustness, logic_communication, lb_logic_communication, ub_logic_communication, conf_index_logic_communication, engaging_with_real_world_impact_quantification_practice_realism_and_relevance, lb_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, ub_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, conf_index_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, relevance_to_global_priorities, lb_relevance_to_global_priorities, ub_relevance_to_global_priorities, conf_index_relevance_to_global_priorities, journal_quality_predict, lb_journal_quality_predict, ub_journal_quality_predict, conf_index_journal_quality_predict, open_collaborative_replicable, conf_index_open_collaborative_replicable, lb_open_collaborative_replicable, ub_open_collaborative_replicable, merits_journal, lb_merits_journal, ub_merits_journal, conf_index_merits_journal)\n\nevals_pub %<>%\n tidyr::unnest_wider(category, names_sep = \"\") %>%\n tidyr::unnest_wider(paper_abbrev, names_sep = \"\") %>%\nmutate(across(everything(), unlist)) %>% #unlist list columns \n dplyr::rename(paper_abbrev = paper_abbrev1)\n\n#Todo -- check the unlist is not propagating the entry\n#Note: category, topic_subfield, and source have multiple meaningful categories. These will need care \n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"Shorten names \"}\nnew_names <- c(\n \"eval_name\" = \"evaluator_name\",\n \"cat_1\" = \"category1\",\n \"cat_2\" = \"category2\",\n \"cat_3\" = \"category3\",\n \"crucial_rsx\" = \"crucial_research\",\n \"conf_overall\" = \"conf_index_overall\",\n \"adv_knowledge\" = \"advancing_knowledge_and_practice\",\n \"lb_adv_knowledge\" = \"lb_advancing_knowledge_and_practice\",\n \"ub_adv_knowledge\" = \"ub_advancing_knowledge_and_practice\",\n \"conf_adv_knowledge\" = \"conf_index_advancing_knowledge_and_practice\",\n \"methods\" = \"methods_justification_reasonableness_validity_robustness\",\n \"lb_methods\" = \"lb_methods_justification_reasonableness_validity_robustness\",\n \"ub_methods\" = \"ub_methods_justification_reasonableness_validity_robustness\",\n \"conf_methods\" = \"conf_index_methods_justification_reasonableness_validity_robustness\",\n \"logic_comms\" = \"logic_communication\",\n \"lb_logic_comms\" = \"lb_logic_communication\",\n \"ub_logic_comms\" = \"ub_logic_communication\",\n \"conf_logic_comms\" = \"conf_index_logic_communication\",\n \"real_world\" = \"engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"lb_real_world\" = \"lb_engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"ub_real_world\" = \"ub_engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"conf_real_world\" = \"conf_index_engaging_with_real_world_impact_quantification_practice_realism_and_relevance\",\n \"gp_relevance\" = \"relevance_to_global_priorities\",\n \"lb_gp_relevance\" = \"lb_relevance_to_global_priorities\",\n \"ub_gp_relevance\" = \"ub_relevance_to_global_priorities\",\n \"conf_gp_relevance\" = \"conf_index_relevance_to_global_priorities\",\n \"journal_predict\" = \"journal_quality_predict\",\n \"lb_journal_predict\" = \"lb_journal_quality_predict\",\n \"ub_journal_predict\" = \"ub_journal_quality_predict\",\n \"conf_journal_predict\" = \"conf_index_journal_quality_predict\",\n \"open_sci\" = \"open_collaborative_replicable\",\n \"conf_open_sci\" = \"conf_index_open_collaborative_replicable\",\n \"lb_open_sci\" = \"lb_open_collaborative_replicable\",\n \"ub_open_sci\" = \"ub_open_collaborative_replicable\",\n \"conf_merits_journal\" = \"conf_index_merits_journal\"\n)\n\nevals_pub <- evals_pub %>%\n rename(!!!new_names)\n\nevals_pub$source_main_wrapped <- wrap_text(evals_pub$source_main, 15)\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in wrap_text(evals_pub$source_main, 15): could not find function \"wrap_text\"\n```\n:::\n\n```{.r .cell-code code-summary=\"Shorten names \"}\nevals_pub$eval_name <- ifelse(\n grepl(\"^\\\\b\\\\w+\\\\b$|\\\\bAnonymous\\\\b\", evals_pub$eval_name),\n paste0(\"Anonymous_\", seq_along(evals_pub$eval_name)),\n evals_pub$eval_name\n)\n\n\n# make the old names into labels\n\n# Create a list of labels\nlabels <- str_replace_all(new_names, \"_\", \" \")\nlabels <- str_to_title(labels)\n \n# Assign labels to the dataframe\n# for(i in seq_along(labels)) {\n# col_name <- new_names[names(new_names)[i]]\n# label <- labels[i]\n# attr(evals_pub[[col_name]], \"label\") <- label\n# }\n# \n```\n:::\n\n\n\n\n### Reconcile uncertainty ratings and CIs {-}\n\nWhere people gave only confidence level 'dots', we impute CIs (confidence/credible intervals). We follow the correspondence described [here](https://effective-giving-marketing.gitbook.io/unjournal-x-ea-and-global-priorities-research/policies-projects-evaluation-workflow/evaluation/guidelines-for-evaluators#1-5-dots-explanation-and-relation-to-cis). (Otherwise where they gave actual CIs, we use these.)^[Note this is only a first-pass; a more sophisticated approach may be warranted in future.]\n\n::: {.callout-note collapse=\"true\"}\n## Dots to interval choices\n\n> 5 = Extremely confident, i.e., 90% confidence interval spans +/- 4 points or less)\n\nFor 0-100 ratings, code the LB as $min(R - 4\\times \\frac{R}{100},0)$ and the UB as $max(R + 4\\times \\frac{R}{100},0)$, where R is the stated (middle) rating. This 'scales' the CI, as interpreted, to be proportional to the rating, with a maximum 'interval' of about 8, with the rating is about 96.\n\n> 4 = Very*confident: 90% confidence interval +/- 8 points or less\n\nFor 0-100 ratings, code the LB as $min(R - 8\\times \\frac{R}{100},0)$ and the UB as $max(R + 8\\times \\frac{R}{100},0)$, where R is the stated (middle) rating. \n\n> 3 = Somewhat** confident: 90% confidence interval +/- 15 points or less \n\n> 2 = Not very** confident: 90% confidence interval, +/- 25 points or less\n\nComparable scaling for the 2-3 ratings as for the 4 and 5 rating.\n\n> 1 = Not** confident: (90% confidence interval +/- more than 25 points)\n \nCode LB as $min(R - 37.5\\times \\frac{R}{100},0)$ and the UB as $max(R + 37.5\\times \\frac{R}{100},0)$. \n \nThis is just a first-pass. There might be a more information-theoretic way of doing this. On the other hand, we might be switching the evaluations to use a different tool soon, perhaps getting rid of the 1-5 confidence ratings altogether.\n\n::: \n\n\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"reconcile explicit bounds and stated confidence level\"}\n# Define the baseline widths for each confidence rating\nbaseline_widths <- c(4, 8, 15, 25, 37.5)\n\n# Define a function to calculate the lower and upper bounds, where given only an index\ncalc_bounds <- function(rating, confidence, lb_explicit, ub_explicit, scale=100) {\n # Check if confidence is NA\n if (is.na(confidence)) {\n return(c(lb_explicit, ub_explicit)) # Return explicit bounds if confidence is NA\n } else {\n baseline_width <- baseline_widths[confidence]\n lb <- pmax(rating - baseline_width * rating / scale, 0)\n ub <- pmin(rating + baseline_width * rating / scale, scale)\n return(c(lb, ub))\n }\n}\n\n# Function to calculate bounds for a single category\ncalc_category_bounds <- function(df, category, scale=100) {\n # Calculate bounds\n bounds <- mapply(calc_bounds, df[[category]], df[[paste0(\"conf_\", category)]], df[[paste0(\"lb_\", category)]], df[[paste0(\"ub_\", category)]])\n \n # Convert to data frame and ensure it has the same number of rows as the input\n bounds_df <- as.data.frame(t(bounds))\n rownames(bounds_df) <- NULL\n \n # Add bounds to original data frame\n df[[paste0(category, \"_lb_imp\")]] <- bounds_df[, 1]\n df[[paste0(category, \"_ub_imp\")]] <- bounds_df[, 2]\n \n return(df)\n}\n\n\n# Lists of categories\n\nrating_cats <- c(\"overall\", \"adv_knowledge\", \"methods\", \"logic_comms\", \"real_world\", \"gp_relevance\", \"open_sci\")\n\n#... 'predictions' are currently 1-5 (0-5?)\npred_cats <- c(\"journal_predict\", \"merits_journal\")\n\n# Apply the function to each category\n# DR: I don't love this looping 'edit in place' code approach, but whatever\nfor (cat in rating_cats) {\n evals_pub <- calc_category_bounds(evals_pub, cat, scale=100)\n}\n\nfor (cat in pred_cats) {\n evals_pub <- calc_category_bounds(evals_pub, cat, scale=5)\n}\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"save data for others' use\"}\nevals_pub %>% saveRDS(file = here(\"data\", \"evals.Rdata\"))\nevals_pub %>% write_csv(file = here(\"data\", \"evals.csv\"))\n\n#evals_pub %>% readRDS(file = here(\"data\", \"evals.Rdata\"))\n```\n:::\n\n\n \n# Basic presentation\n\n## What sorts of papers/projects are we considering and evaluating? \n\nIn this section, we give some simple data summaries and visualizations, for a broad description of The Unjournal's coverage. \n\nIn the interactive tables below we give some key attributes of the papers and the evaluators, and a preview of the evaluations.\n\n\n::: column-body-outset\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n(\n all_evals_dt <- evals_pub %>%\n arrange(paper_abbrev, eval_name) %>%\n dplyr::select(paper_abbrev, crucial_rsx, eval_name, cat_1, cat_2, source_main_wrapped, author_agreement) %>%\n dplyr::select(-matches(\"ub_|lb_|conf\")) %>% \n #rename_all(~ gsub(\"_\", \" \", .)) %>% \n rename(\"Research _____________________\" = \"crucial_rsx\" \n ) %>%\n DT::datatable(\n caption = \"Evaluations (confidence bounds not shown)\", \n filter = 'top',\n rownames= FALSE,\n options = list(pageLength = 7)\n )\n)\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `dplyr::select()`:\n! Can't subset columns that don't exist.\n✖ Column `source_main_wrapped` doesn't exist.\n```\n:::\n:::\n\n\n\n\\\n\nNext, the 'middle ratings and predictions'.\n\n\n::: {.cell}\n\n```{.r .cell-code code-summary=\"Data datable (all shareable relevant data)\"}\n(\n all_evals_dt <- evals_pub %>%\n arrange(paper_abbrev, eval_name, overall) %>%\n dplyr::select(paper_abbrev, eval_name, all_of(rating_cats)) %>%\n DT::datatable(\n caption = \"Evaluations and predictions (confidence bounds not shown)\", \n filter = 'top',\n rownames= FALSE,\n options = list(pageLength = 7)\n )\n)\n```\n\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\\\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n(\n all_evals_dt_ci <- evals_pub %>%\n arrange(paper_abbrev, eval_name) %>%\n dplyr::select(paper_abbrev, eval_name, conf_overall, rating_cats, matches(\"ub_imp|lb_imp\")) %>%\n DT::datatable(\n caption = \"Evaluations and (imputed*) confidence bounds)\", \n filter = 'top',\n rownames= FALSE,\n options = list(pageLength = 7)\n )\n)\n```\n:::\n\n:::\n\n\n::: {.callout-note collapse=\"true\"}\n## Next consider...\n\n- Composition of research evaluated\n - By field (economics, psychology, etc.)\n - By subfield of economics \n - By topic/cause area (Global health, economic development, impact of technology, global catastrophic risks, etc. )\n - By source (submitted, identified with author permission, direct evaluation)\n \n- Timing of intake and evaluation^[Consider: timing might be its own section or chapter; this is a major thing journals track, and we want to keep track of ourselves]\n\n:::\n\nThe funnel plot below starts with the paper we prioritized for likely Unjournal evaluation, marking these as 'considering'.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n#Add in the 3 different evaluation input sources\n#update to be automated rather than hard-coded - to look at David's work here\n\npapers_considered <- all_pub_records %>% nrow()\n\npapers_deprio <- all_pub_records %>% filter(`stage of process/todo` == \"de-prioritized\") %>% nrow()\n\npapers_evaluated <- all_pub_records %>% filter(`stage of process/todo` %in% c(\"published\",\n \"contacting/awaiting_authors_response_to_evaluation\",\n \"awaiting_publication_ME_comments\",\n \"awaiting_evaluations\")) %>% nrow()\n\npapers_complete <- all_pub_records %>% filter(`stage of process/todo` == \"published\") %>% \nnrow()\n\npapers_in_progress <- papers_evaluated-papers_complete\n\npapers_still_in_consideration <- all_pub_records %>% filter(`stage of process/todo` == \"considering\") %>% nrow()\n\n\nfig <- plot_ly(\n type = \"sankey\",\n orientation = \"h\",\n\n node = list(\n label = c(\"Prioritized\", \"Eval uated\", \"Complete\", \"In progress\", \"Still in consideration\", \"De-prioritized\"),\n color = c(\"orange\", \"green\", \"green\", \"orange\", \"orange\", \"red\"),\n pad = 15,\n thickness = 20,\n line = list(\n color = \"black\",\n width = 0.5\n )\n ),\n\n link = list(\n source = c(0,1,1,0,0),\n target = c(1,2,3,4,5),\n value = c(\n papers_evaluated,\n papers_complete,\n papers_in_progress,\n papers_still_in_consideration,\n papers_deprio\n ))\n )\nfig <- fig %>% layout(\n title = \"Unjournal paper funnel\",\n font = list(\n size = 10\n )\n)\n\nfig \n```\n\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary_df <- evals_pub %>%\n distinct(crucial_rsx, .keep_all = T) %>% \n group_by(cat_1) %>%\n summarise(count = n()) \n\nsummary_df$cat_1[is.na(summary_df$cat_1)] <- \"Unknown\"\n\nsummary_df <- summary_df %>%\n arrange(-desc(count)) %>%\n mutate(cat_1 = factor(cat_1, levels = unique(cat_1)))\n\n# Create stacked bar chart\nggplot(summary_df, aes(x = cat_1, y = count)) +\n geom_bar(stat = \"identity\") + \n theme_minimal() +\n labs(x = \"Paper category\", y = \"Count\", \n title = \"Count of evaluated papers by primary category\") \n```\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-14-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# Function to insert a newline character every 15 characters\nwrap_text <- function(x, width = 15) {\n gsub(\"(.{1,15})\", \"\\\\1-\\n\", x)\n}\n\n\n# Bar plot\nggplot(evals_pub, aes(x = source_main_wrapped)) + \n geom_bar(position = \"stack\", stat = \"count\") +\n labs(x = \"Source\", y = \"Count\") +\n theme_light() +\n theme_minimal() +\n ggtitle(\"Evaluations by source of the paper\") + # add title\n theme(\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n text = element_text(size = 16), # changing all text size to 16\n axis.text.y = element_text(size = 10),\n axis.text.x = element_text(size = 14)\n )\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_bar()`:\n! Problem while computing aesthetics.\nℹ Error occurred in the 1st layer.\nCaused by error in `FUN()`:\n! object 'source_main_wrapped' not found\n```\n:::\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-16-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nall_pub_records$is_evaluated = all_pub_records$`stage of process/todo` %in% c(\"published\",\n \"contacting/awaiting_authors_response_to_evaluation\",\n \"awaiting_publication_ME_comments\",\n \"awaiting_evaluations\")\n\nall_pub_records$source_main[all_pub_records$source_main == \"NA\"] <- \"Not applicable\" \nall_pub_records$source_main[all_pub_records$source_main == \"internal-from-syllabus-agenda-policy-database\"] <- \"Internal: syllabus, agenda, etc.\" \nall_pub_records$source_main = tidyr::replace_na(all_pub_records$source_main, \"Unknown\")\n\n\n\nggplot(all_pub_records, aes(x = fct_infreq(source_main), fill = is_evaluated)) + \n geom_bar(position = \"stack\", stat = \"count\") +\n labs(x = \"Source\", y = \"Count\", fill = \"Selected for\\nevaluation?\") +\n coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)\n theme_light() +\n theme_minimal() +\n ggtitle(\"Evaluations by source of the paper\") +# add title\n theme(\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n text = element_text(size = 16), # changing all text size to 16\n axis.text.y = element_text(size = 12),\n axis.text.x = element_text(size = 14)\n )\n```\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-18-1.png){width=672}\n:::\n:::\n\n\n\n### The distribution of ratings and predictions {-}\n\nNext, we present the ratings and predictions along with 'uncertainty measures'.^[We use \"ub imp\" (and \"lb imp\") to denote the upper and lower bounds given by evaluators.] Where evaluators gave only a 1-5 confidence level^[More or less, the ones who report a level for 'conf overall', although some people did this for some but not others], we use the imputations discussed and coded above. \n\n\n- For each category and prediction (overall and by paper)\n\n\n::: column-body-outset\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwrap_text <- function(text, width) {\n sapply(strwrap(text, width = width, simplify = FALSE), paste, collapse = \"\\n\")\n}\n\nevals_pub$wrapped_pub_names <- wrap_text(evals_pub$paper_abbrev, width = 15)\n\n\n\n\n# Dot plot\nggplot(evals_pub, aes(x = paper_abbrev, y = overall)) +\n geom_point(stat = \"identity\", size = 4, shape = 1, colour = \"lightblue\", stroke = 3) +\n geom_text_repel(aes(label = revised_evaluator_name), \n size = 3, \n box.padding = unit(0.35, \"lines\"),\n point.padding = unit(0.3, \"lines\")) +\n coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)\n theme_light() +\n xlab(\"Paper\") + # remove x-axis label\n ylab(\"Overall score\") + # name y-axis\n ggtitle(\"Overall scores of evaluated papers\") +# add title\n theme(\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n text = element_text(size = 14), # changing all text size to 16\n axis.text.y = element_text(size = 8),\n axis.text.x = element_text(size = 12)\n )\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_text_repel()`:\n! Problem while computing aesthetics.\nℹ Error occurred in the 2nd layer.\nCaused by error in `FUN()`:\n! object 'revised_evaluator_name' not found\n```\n:::\n\n::: {.cell-output-display}\n![](evaluation_data_files/figure-html/unnamed-chunk-20-1.png){width=672}\n:::\n:::\n\n:::\n\n\n::: column-body-outset\n\n\n::: {.cell}\n\n```{.r .cell-code}\nunit.scale = function(x) (x*100 - min(x*100)) / (max(x*100) - min(x*100))\nevaluations_table <- evals_pub %>%\n select(paper_abbrev, eval_name, cat_1, source_main, overall, adv_knowledge, methods, logic_comms, journal_predict) %>%\n arrange(desc(paper_abbrev))\n\nout = formattable(\n evaluations_table,\n list(\n #area(col = 5:8) ~ function(x) percent(x / 100, digits = 0),\n area(col = 5:8) ~ color_tile(\"#FA614B66\",\"#3E7DCC\"),\n `journal_predict` = proportion_bar(\"#DeF7E9\", unit.scale)\n )\n)\nout\n```\n\n::: {.cell-output-display}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n
paper_abbrev eval_name cat_1 source_main overall adv_knowledge methods logic_comms journal_predict
Well-being: Cash vs. psychotherapy Anonymous_13 GH&D internal-NBER 90 90 90 80 4.0
Well-being: Cash vs. psychotherapy Hannah Metzler GH&D internal-NBER 75 70 90 75 3.0
Nonprofit Govc.: Randomized healthcare DRC Wayne Aaron Sandholtz GH&D internal-NBER 65 70 60 55 3.6
LT CEA: Resilient foods vs. AGI safety Scott Janzwood long-term-relevant submitted 65 NA NA NA NA
LT CEA: Resilient foods vs. AGI safety Anca Hanea long-term-relevant submitted 80 80 70 85 3.5
LT CEA: Resilient foods vs. AGI safety Alex Bates long-term-relevant submitted 40 30 50 60 2.0
Env. fx of prod.: ecological obs Elias Cisneros NA internal-NBER 88 90 75 80 4.0
Env. fx of prod.: ecological obs Anonymous_12 NA internal-NBER 70 70 70 75 4.0
CBT Human K, Ghana Anonymous_11 NA internal-NBER 75 60 90 70 4.0
CBT Human K, Ghana Anonymous_16 NA internal-NBER 75 65 60 75 NA
Banning wildlife trade can boost demand Anonymous_3 conservation submitted 75 70 80 70 3.0
Banning wildlife trade can boost demand Liew Jia Huan conservation submitted 75 80 50 70 2.5
Advance market commit. (vaccines) David Manheim policy internal-from-syllabus-agenda-policy-database 80 25 95 75 3.0
Advance market commit. (vaccines) Joel Tan policy internal-from-syllabus-agenda-policy-database 79 90 70 70 5.0
Advance market commit. (vaccines) Dan Tortorice policy internal-from-syllabus-agenda-policy-database 80 90 80 80 4.0
AI and econ. growth Seth Benzell macroeconomics internal-from-syllabus-agenda-policy-database 80 75 80 70 NA
AI and econ. growth Phil Trammel macroeconomics internal-from-syllabus-agenda-policy-database 92 97 70 45 3.5
\n:::\n:::\n\n:::\n\n\nNext, look for systematic variation \n\n- By field and topic area of paper\n\n- By submission/selection route\n\n- By evaluation manager\n\n... perhaps building a model of this. We are looking for systematic 'biases and trends', loosely speaking, to help us better understand how our evaluation system is working.\n\n\\\n\n\n\n\n### Relationship among the ratings (and predictions) {-} \n\n- Correlation matrix\n\n- ANOVA\n\n- PCA (Principle components)\n\n- With other 'control' factors?\n\n- How do the specific measures predict the aggregate ones (overall rating, merited publication)\n - CF 'our suggested weighting'\n\n\n## Aggregation of expert opinion (modeling)\n\n\n## Notes on sources and approaches\n\n\n::: {.callout-note collapse=\"true\"}\n\n## Hanea et al {-}\n(Consult, e.g., repliCATS/Hanea and others work; meta-science and meta-analysis approaches)\n\n`aggrecat` package\n\n> Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.\n\n[Hanea et al, 2021](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256919#sec007)\n\n However, these are in a different context. Most of those measures are designed to deal with probablistic forecasts for binary outcomes, where the predictor also gives a 'lower bound' and 'upper bound' for that probability. We could roughly compare that to our continuous metrics with 90% CI's (or imputations for these).\n\nFurthermore, many (all their successful measures?) use 'performance-based weights', accessing metrics from prior prediction performance of the same forecasters We do not have these, nor do we have a sensible proxy for this. \n:::\n\n\n::: {.callout-note collapse=\"true\"}\n## D Veen et al (2017)\n\n[link](https://www.researchgate.net/profile/Duco-Veen/publication/319662351_Using_the_Data_Agreement_Criterion_to_Rank_Experts'_Beliefs/links/5b73e2dc299bf14c6da6c663/Using-the-Data-Agreement-Criterion-to-Rank-Experts-Beliefs.pdf)\n\n... we show how experts can be ranked based on their knowledge and their level of (un)certainty. By letting experts specify their knowledge in the form of a probability distribution, we can assess how accurately they can predict new data, and how appropriate their level of (un)certainty is. The expert’s specified probability distribution can be seen as a prior in a Bayesian statistical setting. We evaluate these priors by extending an existing prior-data (dis)agreement measure, the Data Agreement Criterion, and compare this approach to using Bayes factors to assess prior specification. We compare experts with each other and the data to evaluate their appropriateness. Using this method, new research questions can be asked and answered, for instance: Which expert predicts the new data best? Is there agreement between my experts and the data? Which experts’ representation is more valid or useful? Can we reach convergence between expert judgement and data? We provided an empirical example ranking (regional) directors of a large financial institution based on their predictions of turnover. \n\nBe sure to consult the [correction made here](https://www.semanticscholar.org/paper/Correction%3A-Veen%2C-D.%3B-Stoel%2C-D.%3B-Schalken%2C-N.%3B-K.%3B-Veen-Stoel/a2882e0e8606ef876133f25a901771259e7033b1)\n\n::: \n\n\n::: {.callout-note collapse=\"true\"}\n## Also seems relevant:\n\nSee [Gsheet HERE](https://docs.google.com/spreadsheets/d/14japw6eLGpGjEWy1MjHNJXU1skZY_GAIc2uC2HIUalM/edit#gid=0), generated from an Elicit.org inquiry.\n\n\n::: \n\n\n\nIn spite of the caveats in the fold above, we construct some measures of aggregate beliefs using the `aggrecat` package. We will make (and explain) some ad-hoc choices here. We present these:\n\n1. For each paper\n2. For categories of papers and cross-paper categories of evaluations\n3. For the overall set of papers and evaluations\n\nWe can also hold onto these aggregated metrics for later use in modeling.\n\n\n- Simple averaging\n\n- Bayesian approaches \n\n- Best-performing approaches from elsewhere \n\n- Assumptions over unit-level random terms \n\n### Explicit modeling of 'research quality' (for use in prizes, etc.) {-}\n\n- Use the above aggregation as the outcome of interest, or weight towards categories of greater interest?\n\n- Model with controls -- look for greatest positive residual? \n\n\n## Inter-rater reliability\n\n## Decomposing variation, dimension reduction, simple linear models\n\n\n## Later possiblities\n\n- Relation to evaluation text content (NLP?)\n\n- Relation/prediction of later outcomes (traditional publication, citations, replication)\n\n\n\n## Scoping our future coverage\n\nWe have funding to evaluate roughly 50-70 papers/projects per year, given our proposed incentives.\n\nConsider:\n\n- How many relevant NBER papers come out per year?\n\n- How much relevant work in other prestige archives?\n\n- What quotas do we want (by cause, etc.) and how feasible are these?\n\n", "supporting": [ "evaluation_data_files" ], @@ -10,7 +10,7 @@ ], "includes": { "include-in-header": [ - "\n\n\n\n\n\n\n\n" + "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" ] }, "engineDependencies": {}, diff --git a/_freeze/chapters/evaluation_data/figure-html/unnamed-chunk-14-1.png b/_freeze/chapters/evaluation_data/figure-html/unnamed-chunk-14-1.png index bab83a1109ed5bc5e149b637818bd925bca181e0..da88f0fdf32be1afce20ccbb1f9cc1fdac167303 100644 GIT binary patch literal 61347 zcmeEvXIN9))^0!$umKwd6a*9jK{^UZw}D8L-iy+^Af3=`C@otNLJ@)rp@Z}qKxxtp zy*CM=L+B9dol*BWd!Lj0=l;0QbHD2keHN^+%A9kIImY|GW8~F+RfUts&K-k5ASV^? z-qwIX$mJlABQ=yq!Ee^Bx&|N+vQxIVZr!)PrEtsH!P!;w(F1c!1xqJOS6g!pg_{tF zczC$Bu?@5KX_@%4f-9H4J?%y!-<&$Q_O{SLqvP4&%z=3vtRp3zjnSW5m7u*oFq(`FV%yK zIJSn-zRi-YL+kmR`wAp4N1pd*i8o)Iu!J0(sn0hKB)hTum8$RIHdURZ!#lQ#KWd90 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topic_subfield, and source have multiple meaningful categories. These will need care - ``` @@ -158,10 +156,17 @@ new_names <- c( evals_pub <- evals_pub %>% rename(!!!new_names) +evals_pub$source_main_wrapped <- wrap_text(evals_pub$source_main, 15) + +evals_pub$eval_name <- ifelse( + grepl("^\\b\\w+\\b$|\\bAnonymous\\b", evals_pub$eval_name), + paste0("Anonymous_", seq_along(evals_pub$eval_name)), + evals_pub$eval_name +) + + # make the old names into labels -library(stringr) - # Create a list of labels labels <- str_replace_all(new_names, "_", " ") labels <- str_to_title(labels) @@ -280,18 +285,23 @@ evals_pub %>% write_csv(file = here("data", "evals.csv")) # Basic presentation -## Simple data summaries and visualizations +## What sorts of papers/projects are we considering and evaluating? -Below, we give a data table of key attributes of the paper, the author, and the 'middle' ratings and predictions. +In this section, we give some simple data summaries and visualizations, for a broad description of The Unjournal's coverage. + +In the interactive tables below we give some key attributes of the papers and the evaluators, and a preview of the evaluations. + + +::: column-body-outset + + +```{r } +#| label: datatable0 -```{r eval = FALSE} -#| label: datatable -#| code-summary: "Data datable (all shareable relevant data)" ( all_evals_dt <- evals_pub %>% arrange(paper_abbrev, eval_name) %>% - dplyr::select(paper_abbrev, eval_name, everything())) %>% - dplyr::select(-id) %>% + dplyr::select(paper_abbrev, crucial_rsx, eval_name, cat_1, cat_2, source_main_wrapped, author_agreement) %>% dplyr::select(-matches("ub_|lb_|conf")) %>% #rename_all(~ gsub("_", " ", .)) %>% rename("Research _____________________" = "crucial_rsx" @@ -302,18 +312,41 @@ Below, we give a data table of key attributes of the paper, the author, and the rownames= FALSE, options = list(pageLength = 7) ) +) ``` -Next, we present the ratings and predictions along with 'uncertainty measures'.^[We use "ub imp" (and "lb imp") to denote the upper and lower bounds given by evaluators.] Where evaluators gave only a 1-5 confidence level^[More or less, the ones who report a level for 'conf overall', although some people did this for some but not others], we use the imputations discussed and coded above. -```{r eval = FALSE} +\ + +Next, the 'middle ratings and predictions'. + +```{r } +#| label: datatable +#| code-summary: "Data datable (all shareable relevant data)" + +( + all_evals_dt <- evals_pub %>% + arrange(paper_abbrev, eval_name, overall) %>% + dplyr::select(paper_abbrev, eval_name, all_of(rating_cats)) %>% + DT::datatable( + caption = "Evaluations and predictions (confidence bounds not shown)", + filter = 'top', + rownames= FALSE, + options = list(pageLength = 7) + ) +) + +``` +\ + + + +```{r eval=FALSE} + ( all_evals_dt_ci <- evals_pub %>% - arrange(crucial_rsx, eval_name) %>% - dplyr::select(crucial_rsx, eval_name, conf_overall, matches("ub_imp|lb_imp")) %>% - #rename_all(~ gsub("_", " ", .)) %>% - rename("Research _____________________" = "crucial_rsx" - ) %>% + arrange(paper_abbrev, eval_name) %>% + dplyr::select(paper_abbrev, eval_name, conf_overall, rating_cats, matches("ub_imp|lb_imp")) %>% DT::datatable( caption = "Evaluations and (imputed*) confidence bounds)", filter = 'top', @@ -323,7 +356,11 @@ Next, we present the ratings and predictions along with 'uncertainty measures'.^ ) ``` +::: + +::: {.callout-note collapse="true"} +## Next consider... - Composition of research evaluated - By field (economics, psychology, etc.) @@ -333,6 +370,8 @@ Next, we present the ratings and predictions along with 'uncertainty measures'.^ - Timing of intake and evaluation^[Consider: timing might be its own section or chapter; this is a major thing journals track, and we want to keep track of ourselves] +::: + The funnel plot below starts with the paper we prioritized for likely Unjournal evaluation, marking these as 'considering'. ```{r} @@ -398,68 +437,27 @@ fig ```{r} summary_df <- evals_pub %>% - distinct(crucial_research_unlisted, .keep_all = T) %>% - group_by(category_unlisted) %>% + distinct(crucial_rsx, .keep_all = T) %>% + group_by(cat_1) %>% summarise(count = n()) -summary_df$category_unlisted[is.na(summary_df$category_unlisted)] <- "Unknown" +summary_df$cat_1[is.na(summary_df$cat_1)] <- "Unknown" summary_df <- summary_df %>% arrange(-desc(count)) %>% - mutate(category_unlisted = factor(category_unlisted, levels = unique(category_unlisted))) + mutate(cat_1 = factor(cat_1, levels = unique(cat_1))) # Create stacked bar chart -ggplot(summary_df, aes(x = category_unlisted, y = count)) + +ggplot(summary_df, aes(x = cat_1, y = count)) + geom_bar(stat = "identity") + - coord_flip() + # This makes the chart horizontal theme_minimal() + labs(x = "Paper category", y = "Count", - title = "Count of evaluated papers by category") + title = "Count of evaluated papers by primary category") ``` -### The distribution of ratings and predictions {-} - -- For each category and prediction (overall and by paper) -```{r} - -wrap_text <- function(text, width) { - sapply(strwrap(text, width = width, simplify = FALSE), paste, collapse = "\n") -} - -evals_pub$wrapped_pub_names <- wrap_text(evals_pub$crucial_research_unlisted, width = 60) - - -evals_pub$revised_evaluator_name <- ifelse( - grepl("^\\b\\w+\\b$|\\bAnonymous\\b", evals_pub$evaluator_name_unlisted), - paste0("Anonymous_", seq_along(evals_pub$evaluator_name_unlisted)), - evals_pub$evaluator_name_unlisted -) - - -# Dot plot -ggplot(evals_pub, aes(x = paper_abbrev, y = overall)) + - geom_point(stat = "identity", size = 4, shape = 1, colour = "lightblue", stroke = 3) + - geom_text_repel(aes(label = revised_evaluator_name), - size = 3, - box.padding = unit(0.35, "lines"), - point.padding = unit(0.3, "lines")) + - coord_flip() + # flipping the coordinates to have categories on y-axis (on the left) - theme_light() + - xlab("Paper") + # remove x-axis label - ylab("Overall score") + # name y-axis - ggtitle("Overall scores of evaluated papers") +# add title - theme( - panel.grid.major = element_blank(), - panel.grid.minor = element_blank(), - text = element_text(size = 14), # changing all text size to 16 - axis.text.y = element_text(size = 10), - axis.text.x = element_text(size = 12) - ) -``` - ```{r} # Function to insert a newline character every 15 characters @@ -467,13 +465,11 @@ wrap_text <- function(x, width = 15) { gsub("(.{1,15})", "\\1-\n", x) } -evals_pub$source_main_wrapped <- wrap_text(evals_pub$source_main, 15) # Bar plot ggplot(evals_pub, aes(x = source_main_wrapped)) + geom_bar(position = "stack", stat = "count") + labs(x = "Source", y = "Count") + - coord_flip() + # flipping the coordinates to have categories on y-axis (on the left) theme_light() + theme_minimal() + ggtitle("Evaluations by source of the paper") + # add title @@ -488,6 +484,7 @@ ggplot(evals_pub, aes(x = source_main_wrapped)) + ``` ```{r} + all_pub_records$is_evaluated = all_pub_records$`stage of process/todo` %in% c("published", "contacting/awaiting_authors_response_to_evaluation", "awaiting_publication_ME_comments", @@ -515,14 +512,60 @@ ggplot(all_pub_records, aes(x = fct_infreq(source_main), fill = is_evaluated)) + ) ``` + +### The distribution of ratings and predictions {-} + +Next, we present the ratings and predictions along with 'uncertainty measures'.^[We use "ub imp" (and "lb imp") to denote the upper and lower bounds given by evaluators.] Where evaluators gave only a 1-5 confidence level^[More or less, the ones who report a level for 'conf overall', although some people did this for some but not others], we use the imputations discussed and coded above. + + +- For each category and prediction (overall and by paper) + + +::: column-body-outset + + ```{r} +wrap_text <- function(text, width) { + sapply(strwrap(text, width = width, simplify = FALSE), paste, collapse = "\n") +} + +evals_pub$wrapped_pub_names <- wrap_text(evals_pub$paper_abbrev, width = 15) + + + + +# Dot plot +ggplot(evals_pub, aes(x = paper_abbrev, y = overall)) + + geom_point(stat = "identity", size = 4, shape = 1, colour = "lightblue", stroke = 3) + + geom_text_repel(aes(label = revised_evaluator_name), + size = 3, + box.padding = unit(0.35, "lines"), + point.padding = unit(0.3, "lines")) + + coord_flip() + # flipping the coordinates to have categories on y-axis (on the left) + theme_light() + + xlab("Paper") + # remove x-axis label + ylab("Overall score") + # name y-axis + ggtitle("Overall scores of evaluated papers") +# add title + theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + text = element_text(size = 14), # changing all text size to 16 + axis.text.y = element_text(size = 8), + axis.text.x = element_text(size = 12) + ) +``` +::: + + +::: column-body-outset + +```{r} unit.scale = function(x) (x*100 - min(x*100)) / (max(x*100) - min(x*100)) evaluations_table <- evals_pub %>% - select(crucial_rsx, eval_name, cat_1, source_main, overall, adv_knowledge, methods, logic_comms, journal_predict) %>% - arrange(desc(crucial_rsx)) - + select(paper_abbrev, eval_name, cat_1, source_main, overall, adv_knowledge, methods, logic_comms, journal_predict) %>% + arrange(desc(paper_abbrev)) out = formattable( evaluations_table, @@ -537,8 +580,10 @@ out ``` +::: +Next, look for systematic variation - By field and topic area of paper @@ -546,6 +591,8 @@ out - By evaluation manager +... perhaps building a model of this. 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Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).",Advance market commitments (vaccines),David Manheim,policy,economics,biorisk,internal-from-syllabus-agenda-policy-database,Agreed,80,70,90,NA,25,20,40,NA,95,85,97.5,NA,75,60,90,NA,NA,NA,NA,NA,60,40,75,NA,3,2.5,4.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,70,90,20,40,85,97.5,60,90,NA,NA,40,75,NA,NA,2.5,4.5,NA,NA -recBDa2jHqagZHFa5,Banning wildlife trade can boost demand for unregulated threatened species,Banning wildlife trade can boost demand,Anonymous,conservation,biodiversity,NA,submitted,Agreed,75,NA,NA,NA,70,NA,NA,4,80,NA,NA,3,70,NA,NA,4,90,NA,NA,5,80,3,NA,NA,3,NA,NA,4,70,4,NA,NA,3,NA,NA,4,NA,NA,52.5,87.5,68,92,52.5,87.5,56.25,100,3,NA,52.5,87.5,2.25,3.75,2.25,3.75 -recFWeZjd74NTWvdG,"Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).",Advance market commitments (vaccines),Joel Tan,policy,economics,biorisk,internal-from-syllabus-agenda-policy-database,Agreed,79,59,94,NA,90,70,100,NA,70,50,90,NA,70,50,90,NA,90,70,100,NA,90,70,100,NA,5,NA,NA,5,50,NA,30,70,5,NA,NA,5,59,94,70,100,50,90,50,90,70,100,70,100,30,70,3.125,6.875,3.125,6.875 +recA4IOZhdq2THdVx,"Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).",Advance market commit. (vaccines),David Manheim,policy,economics,biorisk,internal-from-syllabus-agenda-policy-database,Agreed,80,70,90,NA,25,20,40,NA,95,85,97.5,NA,75,60,90,NA,NA,NA,NA,NA,60,40,75,NA,3,2.5,4.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,70,90,20,40,85,97.5,60,90,NA,NA,40,75,NA,NA,2.5,4.5,NA,NA +recBDa2jHqagZHFa5,Banning wildlife trade can boost demand for unregulated threatened species,Banning wildlife trade can boost demand,Anonymous_3,conservation,biodiversity,NA,submitted,Agreed,75,NA,NA,NA,70,NA,NA,4,80,NA,NA,3,70,NA,NA,4,90,NA,NA,5,80,3,NA,NA,3,NA,NA,4,70,4,NA,NA,3,NA,NA,4,NA,NA,52.5,87.5,68,92,52.5,87.5,56.25,100,3,NA,52.5,87.5,2.25,3.75,2.25,3.75 +recFWeZjd74NTWvdG,"Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).",Advance market commit. (vaccines),Joel Tan,policy,economics,biorisk,internal-from-syllabus-agenda-policy-database,Agreed,79,59,94,NA,90,70,100,NA,70,50,90,NA,70,50,90,NA,90,70,100,NA,90,70,100,NA,5,NA,NA,5,50,NA,30,70,5,NA,NA,5,59,94,70,100,50,90,50,90,70,100,70,100,30,70,3.125,6.875,3.125,6.875 recMAQaYQFRL0DQJB,"""The Environmental Effects of Economic Production: Evidence from Ecological Observations (previous title: Economic Production and Biodiversity in the United States)""",Env. fx of prod.: ecological obs,Elias Cisneros,NA,NA,NA,internal-NBER,Agreed,88,NA,NA,5,90,NA,NA,4,75,NA,NA,3,80,NA,NA,4,95,NA,NA,5,95,NA,NA,4,4,NA,NA,5,90,5,NA,NA,4,NA,NA,5,55,100,67.5,100,63.75,86.25,60,100,59.375,100,71.25,100,56.25,100,2.5,5.5,2.5,5.5 -recNMQY75RCZdIcyG,"Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).",Advance market commitments (vaccines),Dan Tortorice,policy,economics,biorisk,internal-from-syllabus-agenda-policy-database,Agreed,80,NA,NA,4,90,NA,NA,5,80,NA,NA,4,80,NA,NA,4,NA,NA,NA,NA,95,NA,NA,5,4,NA,NA,5,90,3,NA,NA,4,NA,NA,5,60,100,56.25,100,60,100,60,100,NA,NA,59.375,100,76.5,100,2.5,5.5,2.5,5.5 +recNMQY75RCZdIcyG,"Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).",Advance market commit. (vaccines),Dan Tortorice,policy,economics,biorisk,internal-from-syllabus-agenda-policy-database,Agreed,80,NA,NA,4,90,NA,NA,5,80,NA,NA,4,80,NA,NA,4,NA,NA,NA,NA,95,NA,NA,5,4,NA,NA,5,90,3,NA,NA,4,NA,NA,5,60,100,56.25,100,60,100,60,100,NA,NA,59.375,100,76.5,100,2.5,5.5,2.5,5.5 recOV0UgplEXwJf81,"Aghion, P., Jones, B.F., and Jones, C.I., 2017. Artificial Intelligence and Economic Growth",AI and econ. growth,Seth Benzell,macroeconomics,Artificial intelligence,prominent,internal-from-syllabus-agenda-policy-database,Agreed,80,70,90,NA,75,65,85,NA,80,75,85,NA,70,60,80,NA,NA,NA,NA,NA,90,85,100,NA,NA,NA,NA,NA,95,NA,90,100,4,3.5,5,NA,70,90,65,85,75,85,60,80,NA,NA,85,100,90,100,NA,NA,3.5,5 recXaqPPgA911nuoY,Banning wildlife trade can boost demand for unregulated threatened species,Banning wildlife trade can boost demand,Liew Jia Huan,conservation,biodiversity,NA,submitted,Agreed,75,NA,NA,4,80,NA,NA,4,50,NA,NA,2,70,NA,NA,5,90,NA,NA,4,65,3,NA,NA,2.5,NA,NA,5,50,5,NA,NA,3,NA,NA,5,56.25,93.75,60,100,46,54,43.75,96.25,67.5,100,3,NA,31.25,68.75,1.5625,3.4375,1.875,4.125 -recYb2JcJlGrHoI2H,The Governance Of Non-Profits And Their Social Impact: Evidence From A Randomized Program In Healthcare In DRC,Governance of nonprofits: Randomized healthcare DRC,Wayne Aaron Sandholtz,GH&D,NA,NA,internal-NBER,Follow-up email sent,65,55,74,NA,70,55,75,NA,60,55,70,NA,55,50,65,NA,55,45,75,NA,80,70,90,NA,3.6,2.8,4,NA,45,NA,30,60,3.8,3,4.1,NA,55,74,55,75,55,70,50,65,45,75,70,90,30,60,2.8,4,3,4.1 +recYb2JcJlGrHoI2H,The Governance Of Non-Profits And Their Social Impact: Evidence From A Randomized Program In Healthcare In DRC,Nonprofit Govc.: Randomized healthcare DRC,Wayne Aaron Sandholtz,GH&D,NA,NA,internal-NBER,Follow-up email sent,65,55,74,NA,70,55,75,NA,60,55,70,NA,55,50,65,NA,55,45,75,NA,80,70,90,NA,3.6,2.8,4,NA,45,NA,30,60,3.8,3,4.1,NA,55,74,55,75,55,70,50,65,45,75,70,90,30,60,2.8,4,3,4.1 recbXm55IKEWH4DAM,"Aghion, P., Jones, B.F., and Jones, C.I., 2017. Artificial Intelligence and Economic Growth ",AI and econ. growth,Phil Trammel,macroeconomics,Artificial intelligence,prominent,internal-from-syllabus-agenda-policy-database,Agreed,92,80,100,NA,97,80,100,NA,70,40,90,NA,45,30,70,NA,NA,NA,NA,NA,92,80,100,NA,3.5,NA,NA,NA,80,1,NA,NA,5,NA,NA,4,80,100,80,100,40,90,30,70,NA,NA,80,100,76.8,83.2,NA,NA,3.75,6.25 -recf9O8DFGO98TPWk,"Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed ""Cognitive Behavioral Therapy among Ghana's Rural Poor Is Effective Regardless of Baseline Mental Distress"")","CBT Human K, Ghana",b62275b05d45f43cce4e494d31a07c19,NA,NA,NA,internal-NBER,Emailed,75,70,84,NA,60,55,65,NA,90,82,94,NA,70,62,82,NA,50,48,52,NA,50,40,60,NA,4,NA,NA,4,90,NA,80,95,4,NA,NA,4,70,84,55,65,82,94,62,82,48,52,40,60,80,95,3,5,3,5 +recf9O8DFGO98TPWk,"Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed ""Cognitive Behavioral Therapy among Ghana's Rural Poor Is Effective Regardless of Baseline Mental Distress"")","CBT Human K, Ghana",Anonymous_11,NA,NA,NA,internal-NBER,Emailed,75,70,84,NA,60,55,65,NA,90,82,94,NA,70,62,82,NA,50,48,52,NA,50,40,60,NA,4,NA,NA,4,90,NA,80,95,4,NA,NA,4,70,84,55,65,82,94,62,82,48,52,40,60,80,95,3,5,3,5 recifgZ3CxEQaz3m4,"""The Environmental Effects of Economic Production: Evidence from Ecological Observations - (previous title: Economic Production and Biodiversity in the United States)""",Env. fx of prod.: ecological obs,1ef6aff67012a1750f88f631fddb346c,NA,NA,NA,internal-NBER,Agreed,70,NA,NA,3,70,NA,NA,3,70,NA,NA,3,75,NA,NA,4,60,NA,NA,3,80,NA,NA,4,4,NA,NA,3,65,2,NA,NA,4,NA,NA,5,59.5,80.5,59.5,80.5,59.5,80.5,56.25,93.75,51,69,60,100,59.8,70.2,3.4,4.6,2.5,5.5 -reck0WYhoffiyWRGH,The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being,Well-being: Cash transfers vs. psychotherapy,Anonymous Reviewer 1,GH&D,NA,NA,internal-NBER,Acknowledged,90,NA,NA,3,90,NA,NA,2,90,NA,NA,3,80,NA,NA,4,100,NA,NA,5,100,NA,NA,5,4,NA,NA,3,70,5,NA,NA,5,NA,NA,5,76.5,100,82.8,97.2,76.5,100,60,100,62.5,100,62.5,100,43.75,96.25,3.4,4.6,3.125,6.875 -recmEzlRNxLNEWD8e,The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being,Well-being: Cash transfers vs. psychotherapy,Hannah Metzler,GH&D,NA,NA,internal-NBER,Acknowledged,75,65,85,NA,70,60,90,NA,90,85,95,NA,75,70,90,NA,75,60,90,NA,90,80,100,NA,3,2,4,NA,50,NA,40,80,4,3,4,NA,65,85,60,90,85,95,70,90,60,90,80,100,40,80,2,4,3,4 + (previous title: Economic Production and Biodiversity in the United States)""",Env. fx of prod.: ecological obs,Anonymous_12,NA,NA,NA,internal-NBER,Agreed,70,NA,NA,3,70,NA,NA,3,70,NA,NA,3,75,NA,NA,4,60,NA,NA,3,80,NA,NA,4,4,NA,NA,3,65,2,NA,NA,4,NA,NA,5,59.5,80.5,59.5,80.5,59.5,80.5,56.25,93.75,51,69,60,100,59.8,70.2,3.4,4.6,2.5,5.5 +reck0WYhoffiyWRGH,The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being,Well-being: Cash vs. psychotherapy,Anonymous_13,GH&D,NA,NA,internal-NBER,Acknowledged,90,NA,NA,3,90,NA,NA,2,90,NA,NA,3,80,NA,NA,4,100,NA,NA,5,100,NA,NA,5,4,NA,NA,3,70,5,NA,NA,5,NA,NA,5,76.5,100,82.8,97.2,76.5,100,60,100,62.5,100,62.5,100,43.75,96.25,3.4,4.6,3.125,6.875 +recmEzlRNxLNEWD8e,The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being,Well-being: Cash vs. psychotherapy,Hannah Metzler,GH&D,NA,NA,internal-NBER,Acknowledged,75,65,85,NA,70,60,90,NA,90,85,95,NA,75,70,90,NA,75,60,90,NA,90,80,100,NA,3,2,4,NA,50,NA,40,80,4,3,4,NA,65,85,60,90,85,95,70,90,60,90,80,100,40,80,2,4,3,4 recrdCChKWw5gZr4e,Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al),LT CEA: Resilient foods vs. AGI safety,Anca Hanea,long-term-relevant ,quantitative,NA,submitted,Agreed,80,60,90,NA,80,70,90,NA,70,50,90,NA,85,65,95,NA,NA,NA,NA,NA,85,70,90,NA,3.5,3,5,NA,73,NA,50,95,4,3,5,NA,60,90,70,90,50,90,65,95,NA,NA,70,90,50,95,3,5,3,5 -recscTNMRp1wYdD26,"Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed ""Cognitive Behavioral Therapy among Ghana's Rural Poor Is Effective Regardless of Baseline Mental Distress"")","CBT Human K, Ghana",47273de4862aaff608f9086d4d643054,NA,NA,NA,internal-NBER,Emailed,75,NA,NA,4,65,NA,NA,4,60,NA,NA,3,75,NA,NA,3,75,NA,NA,4,75,NA,NA,3,NA,NA,NA,NA,50,3,NA,NA,NA,NA,NA,NA,56.25,93.75,48.75,81.25,51,69,63.75,86.25,56.25,93.75,63.75,86.25,42.5,57.5,NA,NA,NA,NA +recscTNMRp1wYdD26,"Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed ""Cognitive Behavioral Therapy among Ghana's Rural Poor Is Effective Regardless of Baseline Mental Distress"")","CBT Human K, Ghana",Anonymous_16,NA,NA,NA,internal-NBER,Emailed,75,NA,NA,4,65,NA,NA,4,60,NA,NA,3,75,NA,NA,3,75,NA,NA,4,75,NA,NA,3,NA,NA,NA,NA,50,3,NA,NA,NA,NA,NA,NA,56.25,93.75,48.75,81.25,51,69,63.75,86.25,56.25,93.75,63.75,86.25,42.5,57.5,NA,NA,NA,NA recsivZraxfYLKEqJ,Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al),LT CEA: Resilient foods vs. AGI safety,Alex Bates,long-term-relevant ,quantitative,NA,submitted,Agreed,40,20,60,NA,30,20,60,NA,50,40,60,NA,60,40,75,NA,NA,NA,NA,NA,90,60,95,NA,2,1,2,NA,70,NA,40,75,2,1,2,NA,20,60,20,60,40,60,40,75,NA,NA,60,95,40,75,1,2,1,2 diff --git a/docs/chapters/evaluation_data.html b/docs/chapters/evaluation_data.html index e6cde8f..eadf40b 100644 --- a/docs/chapters/evaluation_data.html +++ b/docs/chapters/evaluation_data.html @@ -116,8 +116,13 @@ "search-detached-cancel-button-title": "Cancel", "search-submit-button-title": "Submit" } -} - +} + + + + + + @@ -169,12 +174,12 @@

-
+

1  Evaluation data: description, exploration, checks

@@ -248,12 +253,13 @@

#library(rethinking)

+
-
install aggrecat package
#devtools::install_github("metamelb-repliCATS/aggreCAT")
-
-
-
-
input from airtable
base_id <- "appbPYEw9nURln7Qg"
+
input from airtable
base_id <- "appbPYEw9nURln7Qg"
 
 # Set your Airtable API key
 #Sys.setenv(AIRTABLE_API_KEY = "") 
@@ -264,7 +270,6 @@ 

 
 evals <- air_get(base = base_id, "output_eval") 
 
-
 all_pub_records <- data.frame()
 pub_records <- air_select(base = base_id, table = "crucial_research")
 
@@ -285,27 +290,26 @@
-
just the useful and publish-able data, clean a bit
colnames(evals) <- snakecase::to_snake_case(colnames(evals))
+
just the useful and publish-able data, clean a bit
colnames(evals) <- snakecase::to_snake_case(colnames(evals))
 
 evals_pub <- evals %>% 
   dplyr::rename(stage_of_process = stage_of_process_todo_from_crucial_research_2) %>% 
   mutate(stage_of_process = unlist(stage_of_process)) %>% 
   dplyr::filter(stage_of_process == "published") %>% 
-    select(id, crucial_research, evaluator_name, category, source_main, author_agreement, overall, lb_overall, ub_overall, conf_index_overall, advancing_knowledge_and_practice, lb_advancing_knowledge_and_practice, ub_advancing_knowledge_and_practice, conf_index_advancing_knowledge_and_practice, methods_justification_reasonableness_validity_robustness, lb_methods_justification_reasonableness_validity_robustness, ub_methods_justification_reasonableness_validity_robustness, conf_index_methods_justification_reasonableness_validity_robustness, logic_communication, lb_logic_communication, ub_logic_communication, conf_index_logic_communication, engaging_with_real_world_impact_quantification_practice_realism_and_relevance, lb_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, ub_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, conf_index_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, relevance_to_global_priorities, lb_relevance_to_global_priorities, ub_relevance_to_global_priorities, conf_index_relevance_to_global_priorities, journal_quality_predict, lb_journal_quality_predict, ub_journal_quality_predict, conf_index_journal_quality_predict, open_collaborative_replicable, conf_index_open_collaborative_replicable, lb_open_collaborative_replicable, ub_open_collaborative_replicable, merits_journal, lb_merits_journal, ub_merits_journal, conf_index_merits_journal)
+    select(id, crucial_research, paper_abbrev, evaluator_name, category, source_main, author_agreement, overall, lb_overall, ub_overall, conf_index_overall, advancing_knowledge_and_practice, lb_advancing_knowledge_and_practice, ub_advancing_knowledge_and_practice, conf_index_advancing_knowledge_and_practice, methods_justification_reasonableness_validity_robustness, lb_methods_justification_reasonableness_validity_robustness, ub_methods_justification_reasonableness_validity_robustness, conf_index_methods_justification_reasonableness_validity_robustness, logic_communication, lb_logic_communication, ub_logic_communication, conf_index_logic_communication, engaging_with_real_world_impact_quantification_practice_realism_and_relevance, lb_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, ub_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, conf_index_engaging_with_real_world_impact_quantification_practice_realism_and_relevance, relevance_to_global_priorities, lb_relevance_to_global_priorities, ub_relevance_to_global_priorities, conf_index_relevance_to_global_priorities, journal_quality_predict, lb_journal_quality_predict, ub_journal_quality_predict, conf_index_journal_quality_predict, open_collaborative_replicable, conf_index_open_collaborative_replicable, lb_open_collaborative_replicable, ub_open_collaborative_replicable, merits_journal, lb_merits_journal, ub_merits_journal, conf_index_merits_journal)
 
 evals_pub %<>%
-mutate(across(everything(), ~ map(.x, ~ ifelse(is.null(.x), NA, .x)), .names = "{.col}_unlisted")) %>%  # for each co
  tidyr::unnest_wider(category, names_sep = "") %>%
-mutate(across(everything(), unlist)) #unlist list columns
-
+ tidyr::unnest_wider(paper_abbrev, names_sep = "") %>%
+mutate(across(everything(), unlist)) %>%  #unlist list columns 
+  dplyr::rename(paper_abbrev = paper_abbrev1)
 
 #Todo -- check the unlist is not propagating the entry
-
 #Note: category,  topic_subfield, and source have multiple meaningful categories. These will need care
-
Shorten names
new_names <- c(
+
Shorten names
new_names <- c(
   "eval_name" = "evaluator_name",
   "cat_1" = "category1",
   "cat_2" = "category2",
@@ -346,13 +350,22 @@ 

 evals_pub <- evals_pub %>%
   rename(!!!new_names)
 
+evals_pub$source_main_wrapped <- wrap_text(evals_pub$source_main, 15)

+

+
Error in wrap_text(evals_pub$source_main, 15): could not find function "wrap_text"

+

+
Shorten names
evals_pub$eval_name <- ifelse(
+  grepl("^\\b\\w+\\b$|\\bAnonymous\\b", evals_pub$eval_name),
+  paste0("Anonymous_", seq_along(evals_pub$eval_name)),
+  evals_pub$eval_name
+)
+
+
 # make the old names into labels
 
-library(stringr)
- 
 #  Create a list of labels
-labels <- str_replace_all(new_names, "_", " ")
-labels <- str_to_title(labels)
+labels <- str_replace_all(new_names, "_", " ")
+labels <- str_to_title(labels)
  
 # Assign labels to the dataframe
 # for(i in seq_along(labels)) {
@@ -365,8 +378,8 @@ 

 

 
 
-
Reconcile the uncertainty ratings and CIs (first-pass)

-
Impute CIs from stated confidence level ‘dots’, correspondence loosely described here

+
Reconcile uncertainty ratings and CIs

+
Where people gave only confidence level ‘dots’, we impute CIs (confidence/credible intervals). We follow the correspondence described here. (Otherwise where they gave actual CIs, we use these.)1

 

 
 

 

-
reconcile explicit bounds and stated confidence level
# Define the baseline widths for each confidence rating
+
reconcile explicit bounds and stated confidence level
# Define the baseline widths for each confidence rating
 baseline_widths <- c(4, 8, 15, 25, 37.5)
 
 # Define a function to calculate the lower and upper bounds, where given only an index
@@ -455,23 +468,24 @@ 

 

 

 

-
save data for others’ use
evals_pub %>% saveRDS(file = here("data", "evals.Rdata"))
+
save data for others’ use
evals_pub %>% saveRDS(file = here("data", "evals.Rdata"))
 evals_pub %>% write_csv(file = here("data", "evals.csv"))
 
 #evals_pub %>% readRDS(file = here("data", "evals.Rdata"))

 

 

-

+

 2 Basic presentation

-

-2.1 Simple data summaries/codebooks/dashboards and visualization

-
Below, we give a data table of key attributes of the paper, the author, and the ‘middle’ ratings and predictions.

+

+2.1 What sorts of papers/projects are we considering and evaluating?

+
In this section, we give some simple data summaries and visualizations, for a broad description of The Unjournal’s coverage.

+
In the interactive tables below we give some key attributes of the papers and the evaluators, and a preview of the evaluations.

+

 

-
Data datable (all shareable relevant data)
(
+
Code
(
   all_evals_dt <- evals_pub %>%
-  arrange(crucial_rsx, eval_name) %>%
-  dplyr::select(crucial_rsx, eval_name, everything())) %>%
-  dplyr::select(-id) %>% 
+  arrange(paper_abbrev, eval_name) %>%
+  dplyr::select(paper_abbrev, crucial_rsx, eval_name, cat_1, cat_2, source_main_wrapped, author_agreement) %>%
     dplyr::select(-matches("ub_|lb_|conf")) %>% 
     #rename_all(~ gsub("_", " ", .)) %>% 
     rename("Research  _____________________" = "crucial_rsx" 
@@ -481,18 +495,41 @@ 

     filter = 'top',
     rownames= FALSE,
     options = list(pageLength = 7)
-    )

-

+    )
+)

+

+
Error in `dplyr::select()`:
+! Can't subset columns that don't exist.
+✖ Column `source_main_wrapped` doesn't exist.

+

 

-
Next, we present the ratings and predictions along with ‘uncertainty measures’. We use “ub imp” (and “lb imp”) to denote the upper and lower bounds given by evaluators. Where evaluators gave only a 1-5 confidence level1, we use the imputations discussed and coded above.

+


+
Next, the ‘middle ratings and predictions’.

 

-
Code
(
+
Data datable (all shareable relevant data)
(
+  all_evals_dt <- evals_pub %>%
+  arrange(paper_abbrev, eval_name, overall) %>%
+  dplyr::select(paper_abbrev, eval_name, all_of(rating_cats))  %>%
+  DT::datatable(
+    caption = "Evaluations and predictions (confidence bounds not shown)", 
+    filter = 'top',
+    rownames= FALSE,
+    options = list(pageLength = 7)
+    )
+)

+

+
+

+
+

+

+


+
+

+
Code
(
   all_evals_dt_ci <- evals_pub %>%
-  arrange(crucial_rsx, eval_name) %>%
-  dplyr::select(crucial_rsx, eval_name, conf_overall, matches("ub_imp|lb_imp")) %>%
-    #rename_all(~ gsub("_", " ", .)) %>% 
-    rename("Research  _____________________" = "crucial_rsx" 
-      ) %>%
+  arrange(paper_abbrev, eval_name) %>%
+  dplyr::select(paper_abbrev, eval_name, conf_overall, rating_cats, matches("ub_imp|lb_imp")) %>%
   DT::datatable(
     caption = "Evaluations and (imputed*) confidence bounds)", 
     filter = 'top',
@@ -502,6 +539,19 @@ 

 )

 

 

+

+

+
+

+

 
    
 
  • Composition of research evaluated
 
      
@@ -511,20 +561,32 @@ 
      
 
    • By source (submitted, identified with author permission, direct evaluation)
      • 
 
    
 
    • 
-
  • Timing of intake and evaluation (Consider: timing might be its own section or chapter; this is a major thing journals track, and we want to keep track of ourselves)
    • 
+
  • Timing of intake and evaluation2
+
    • 
 

+

+

+

+
The funnel plot below starts with the paper we prioritized for likely Unjournal evaluation, marking these as ‘considering’.

 

-
Code
#Add in the 3 different evaluation input sources
+
Code
#Add in the 3 different evaluation input sources
 #update to be automated rather than hard-coded - to look at David's work here
-papers_considered = all_pub_records %>%nrow()
-papers_deprio = all_pub_records %>% filter(`stage of process/todo` ==  "de-prioritized") %>%nrow()
-papers_evaluated = all_pub_records %>% filter(`stage of process/todo` %in%  c("published",
+
+papers_considered <- all_pub_records %>% nrow()
+
+papers_deprio <- all_pub_records %>% filter(`stage of process/todo` ==  "de-prioritized") %>% nrow()
+
+papers_evaluated <- all_pub_records %>% filter(`stage of process/todo` %in%  c("published",
                                                                               "contacting/awaiting_authors_response_to_evaluation",
                                                                                "awaiting_publication_ME_comments",
-                                                                              "awaiting_evaluations")) %>%nrow()
-papers_complete = all_pub_records %>% filter(`stage of process/todo` ==  "published") %>% nrow()
-papers_in_progress = papers_evaluated-papers_complete
-papers_still_in_consideration = all_pub_records %>% filter(`stage of process/todo` ==  "considering") %>%nrow()
+                                                                              "awaiting_evaluations")) %>% nrow()
+
+papers_complete <- all_pub_records %>% filter(`stage of process/todo` ==  "published") %>% 
+nrow()
+
+papers_in_progress <-  papers_evaluated-papers_complete
+
+papers_still_in_consideration <-  all_pub_records %>% filter(`stage of process/todo` ==  "considering") %>% nrow()
 
 
 fig <- plot_ly(
@@ -532,7 +594,7 @@ 

     orientation = "h",
 
     node = list(
-      label = c("All paper considered", "Papers evaluated", "Papers complete", "Papers in progress", "Papers still in consideration", "Papers rejected"),
+      label = c("Prioritized", "Eval    uated", "Complete", "In progress", "Still in consideration", "De-prioritized"),
       color = c("orange", "green", "green", "orange", "orange", "red"),
       pad = 15,
       thickness = 20,
@@ -562,119 +624,43 @@ 

 
 fig 

 

-

-
+

+
 

 

-
The distribution of ratings and predictions

-
    
-
  • For each category and prediction (overall and by paper)
    • 
-

 

-
Code
summary_df <- evals_pub %>%
-  distinct(crucial_research_unlisted, .keep_all = T) %>% 
-  group_by(category_unlisted) %>%
+
Code
summary_df <- evals_pub %>%
+  distinct(crucial_rsx, .keep_all = T) %>% 
+  group_by(cat_1) %>%
   summarise(count = n()) 
 
-summary_df$category_unlisted[is.na(summary_df$category_unlisted)] <- "Unknown"
+summary_df$cat_1[is.na(summary_df$cat_1)] <- "Unknown"
 
 summary_df <- summary_df %>%
   arrange(-desc(count)) %>%
-  mutate(category_unlisted = factor(category_unlisted, levels = unique(category_unlisted)))
+  mutate(cat_1 = factor(cat_1, levels = unique(cat_1)))
 
 # Create stacked bar chart
-ggplot(summary_df, aes(x = category_unlisted, y = count)) +
+ggplot(summary_df, aes(x = cat_1, y = count)) +
   geom_bar(stat = "identity") + 
-  coord_flip() + # This makes the chart horizontal
   theme_minimal() +
   labs(x = "Paper category", y = "Count", 
-       title = "Count of evaluated papers by category") 

+       title = "Count of evaluated papers by primary category") 

 

 

 

 

 

-
Code
wrap_text <- function(text, width) {
-  sapply(strwrap(text, width = width, simplify = FALSE), paste, collapse = "\n")
-}
-
-evals_pub$wrapped_pub_names <- wrap_text(evals_pub$crucial_research_unlisted, width = 60)
-
-
-# original names
-original_names <- evals_pub$crucial_research_unlisted
-
-# shortened names
-shortened_names <- c("Resilient Foods vs AGI Safety",
-                     "Advance Market Commitments",
-                     "Wildlife Trade Demand",
-                     "Advance Market Commitments",
-                     "Economic Prod. & Biodiversity",
-                     "Advance Market Commitments",
-                     "AI and Economic Growth",
-                     "Wildlife Trade Demand",
-                     "Non-Profits Governance & Impact",
-                     "AI and Economic Growth",
-                     "Mental Health Therapy & Human Capital",
-                     "Economic Prod. & Biodiversity",
-                     "Cash Transfers vs Psychotherapy",
-                     "Cash Transfers vs Psychotherapy",
-                     "Resilient Foods vs AGI Safety",
-                     "Mental Health Therapy & Human Capital",
-                     "Resilient Foods vs AGI Safety")
-
-# create a named vector for easy lookup
-name_lookup <- setNames(shortened_names, original_names)
-
-# use the lookup to create the new column
-evals_pub$shortened_names <- name_lookup[evals_pub$crucial_research_unlisted]
-
-evals_pub$wrapped_shortened_names <- wrap_text(evals_pub$shortened_names, width = 15)
-
-#Move this to do this 'cleaning' earlier
-evals_pub$revised_evaluator_name <- ifelse(
-  grepl("^\\b\\w+\\b$|\\bAnonymous\\b", evals_pub$evaluator_name_unlisted),
-  paste0("Anonymous_", seq_along(evals_pub$evaluator_name_unlisted)),
-  evals_pub$evaluator_name_unlisted
-)
-
-
-# Dot plot
-ggplot(evals_pub, aes(x = shortened_names, y = overall)) +
-  geom_point(stat = "identity", size = 4, shape = 1, colour = "lightblue", stroke = 3) +
-  geom_text_repel(aes(label = revised_evaluator_name), 
-                  size = 3, 
-                  box.padding = unit(0.35, "lines"),
-                  point.padding = unit(0.3, "lines")) +
-  coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)
-  theme_light() +
-  xlab("Paper") + # remove x-axis label
-  ylab("Overall score") + # name y-axis
-  ggtitle("Overall scores of evaluated papers") +# add title
-  theme(
-    panel.grid.major = element_blank(),
-    panel.grid.minor = element_blank(),
-    text = element_text(size = 14), # changing all text size to 16
-    axis.text.y = element_text(size = 10),
-    axis.text.x = element_text(size = 12)
-  )

-

-

-

-

-

-
Code
# Function to insert a newline character every 15 characters
+
Code
# Function to insert a newline character every 15 characters
 wrap_text <- function(x, width = 15) {
   gsub("(.{1,15})", "\\1-\n", x)
 }
 
-evals_pub$source_main_wrapped <- wrap_text(evals_pub$source_main, 15)
 
 # Bar plot
 ggplot(evals_pub, aes(x = source_main_wrapped)) + 
   geom_bar(position = "stack", stat = "count") +
   labs(x = "Source", y = "Count") +
-  coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)
   theme_light() +
   theme_minimal() +
   ggtitle("Evaluations by source of the paper") + # add title
@@ -685,12 +671,19 @@ 

     axis.text.y = element_text(size = 10),
     axis.text.x = element_text(size = 14)
   )

-

-

+

+
Error in `geom_bar()`:
+! Problem while computing aesthetics.
+ℹ Error occurred in the 1st layer.
+Caused by error in `FUN()`:
+! object 'source_main_wrapped' not found

+

+

+

 

 

 

-
Code
all_pub_records$is_evaluated = all_pub_records$`stage of process/todo` %in%  c("published",
+
Code
all_pub_records$is_evaluated = all_pub_records$`stage of process/todo` %in%  c("published",
                                                                               "contacting/awaiting_authors_response_to_evaluation",
                                                                                "awaiting_publication_ME_comments",
                                                                               "awaiting_evaluations")
@@ -716,15 +709,62 @@ 

     axis.text.x = element_text(size = 14)
     )

 

+

+

+

+
The distribution of ratings and predictions

+
Next, we present the ratings and predictions along with ‘uncertainty measures’.3 Where evaluators gave only a 1-5 confidence level4, we use the imputations discussed and coded above.

+
    
+
  • For each category and prediction (overall and by paper)
    • 
+

+

+

+
Code
wrap_text <- function(text, width) {
+  sapply(strwrap(text, width = width, simplify = FALSE), paste, collapse = "\n")
+}
+
+evals_pub$wrapped_pub_names <- wrap_text(evals_pub$paper_abbrev, width = 15)
+
+
+
+
+# Dot plot
+ggplot(evals_pub, aes(x = paper_abbrev, y = overall)) +
+  geom_point(stat = "identity", size = 4, shape = 1, colour = "lightblue", stroke = 3) +
+  geom_text_repel(aes(label = revised_evaluator_name), 
+                  size = 3, 
+                  box.padding = unit(0.35, "lines"),
+                  point.padding = unit(0.3, "lines")) +
+  coord_flip() + # flipping the coordinates to have categories on y-axis (on the left)
+  theme_light() +
+  xlab("Paper") + # remove x-axis label
+  ylab("Overall score") + # name y-axis
+  ggtitle("Overall scores of evaluated papers") +# add title
+  theme(
+    panel.grid.major = element_blank(),
+    panel.grid.minor = element_blank(),
+    text = element_text(size = 14), # changing all text size to 16
+    axis.text.y = element_text(size = 8),
+    axis.text.x = element_text(size = 12)
+  )

+

+
Error in `geom_text_repel()`:
+! Problem while computing aesthetics.
+ℹ Error occurred in the 2nd layer.
+Caused by error in `FUN()`:
+! object 'revised_evaluator_name' not found

+

+

 

 

 

+

+

 

-
Code
unit.scale = function(x) (x*100 - min(x*100)) / (max(x*100) - min(x*100))
+
Code
unit.scale = function(x) (x*100 - min(x*100)) / (max(x*100) - min(x*100))
 evaluations_table <- evals_pub %>%
-  select(crucial_rsx, eval_name, cat_1, source_main, overall, adv_knowledge, methods, logic_comms, journal_predict) %>%
-  arrange(desc(crucial_rsx))
-
+  select(paper_abbrev, eval_name, cat_1, source_main, overall, adv_knowledge, methods, logic_comms, journal_predict) %>%
+  arrange(desc(paper_abbrev))
 
 out = formattable(
   evaluations_table,
@@ -740,7 +780,7 @@ 

 
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+

 

 
-crucial_rsx
+paper_abbrev
 
 
 eval_name
@@ -770,39 +810,10 @@ 

 

 

 
-The Governance Of Non-Profits And Their Social Impact: Evidence From A Randomized Program In Healthcare In DRC
-
-Wayne Aaron Sandholtz
-
-GH&D
-
-internal-NBER
-
-65
-
-70
-
-60
-
-55
-
-3.6
-

-The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being
+Well-being: Cash vs. psychotherapy
 
 
-Anonymous Reviewer 1
+Anonymous_13
 
 
 GH&D
@@ -828,7 +839,7 @@ 

 

 

 
-The Comparative Impact of Cash Transfers and a Psychotherapy Program on Psychological and Economic Well-being
+Well-being: Cash vs. psychotherapy
 
 
 Hannah Metzler
@@ -857,65 +868,36 @@ 

 

 

 
-Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed “Cognitive Behavioral Therapy among Ghana’s Rural Poor Is Effective Regardless of Baseline Mental Distress”)
+Nonprofit Govc.: Randomized healthcare DRC
 
 
-b62275b05d45f43cce4e494d31a07c19
+Wayne Aaron Sandholtz
 
 
-NA
+GH&D
 
 
 internal-NBER
 
 
-75
-
-60
-
-90
+65
 
 
 70
 
 
-4.0
-

-Mental Health Therapy as a Core Strategy for Increasing Human Capital: Evidence from Ghana (renamed “Cognitive Behavioral Therapy among Ghana’s Rural Poor Is Effective Regardless of Baseline Mental Distress”)
-
-47273de4862aaff608f9086d4d643054
-
-NA
-
-internal-NBER
-
-75
-
-65
-
 60
 
 
-75
+55
 
 
-NA
+3.6
 
 

 

 
-Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al)
+LT CEA: Resilient foods vs. AGI safety
 
 
 Scott Janzwood
@@ -944,7 +926,7 @@ 

 

 

 
-Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al)
+LT CEA: Resilient foods vs. AGI safety
 
 
 Anca Hanea
@@ -973,7 +955,7 @@ 

 

 

 
-Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety (Denkenberger et al)
+LT CEA: Resilient foods vs. AGI safety
 
 
 Alex Bates
@@ -1002,97 +984,126 @@ 

 

 

 
-Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).
+Env. fx of prod.: ecological obs
 
 
-David Manheim
+Elias Cisneros
 
 
-policy
+NA
 
 
-internal-from-syllabus-agenda-policy-database
+internal-NBER
+
+88
+
+90
+
+75
 
 
 80
 
 
-25
+4.0
 

 
-95
+Env. fx of prod.: ecological obs
+
+Anonymous_12
+
+NA
+
+internal-NBER
+
+70
+
+70
+
+70
 
 
 75
 
 
-3.0
+4.0
 
 

 

 
-Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).
+CBT Human K, Ghana
 
 
-Joel Tan
+Anonymous_11
 
 
-policy
+NA
 
 
-internal-from-syllabus-agenda-policy-database
+internal-NBER
 
 
-79
+75
 
 
-90
+60
 
 
-70
+90
 
 
 70
 
 
-5.0
+4.0
 
 

 

 
-Kremer, M., Levin, J. and Snyder, C.M., 2020, May. Advance Market Commitments: Insights from Theory and Experience. In AEA Papers and Proceedings (Vol. 110, pp. 269-73).
+CBT Human K, Ghana
 
 
-Dan Tortorice
+Anonymous_16
 
 
-policy
+NA
 
 
-internal-from-syllabus-agenda-policy-database
+internal-NBER
 
 
-80
+75
 
 
-90
+65
 
 
-80
+60
 
 
-80
+75
 
 
-4.0
+NA
 
 

 

 
-Banning wildlife trade can boost demand for unregulated threatened species
+Banning wildlife trade can boost demand
 
 
-Anonymous
+Anonymous_3
 
 
 conservation
@@ -1118,7 +1129,7 @@ 

 

 

 
-Banning wildlife trade can boost demand for unregulated threatened species
+Banning wildlife trade can boost demand
 
 
 Liew Jia Huan
@@ -1147,83 +1158,83 @@ 

 

 

 
-Aghion, P., Jones, B.F., and Jones, C.I., 2017. Artificial Intelligence and Economic Growth
+Advance market commit. (vaccines)
 
 
-Phil Trammel
+David Manheim
 
 
-macroeconomics
+policy
 
 
 internal-from-syllabus-agenda-policy-database
 
 
-92
+80
 
 
-97
+25
 
 
-70
+95
 
 
-45
+75
 
 
-3.5
+3.0
 
 

 

 
-Aghion, P., Jones, B.F., and Jones, C.I., 2017. Artificial Intelligence and Economic Growth
+Advance market commit. (vaccines)
 
 
-Seth Benzell
+Joel Tan
 
 
-macroeconomics
+policy
 
 
 internal-from-syllabus-agenda-policy-database
 
 
-80
+79
 
 
-75
+90
 
 
-80
+70
 
 
 70
 
 
-NA
+5.0
 
 

 

 
-“The Environmental Effects of Economic Production: Evidence from Ecological Observations (previous title: Economic Production and Biodiversity in the United States)”
+Advance market commit. (vaccines)
 
 
-Elias Cisneros
+Dan Tortorice
 
 
-NA
+policy
 
 
-internal-NBER
+internal-from-syllabus-agenda-policy-database
 
 
-88
+80
 
 
 90
 
 
-75
+80
 
 
 80
@@ -1234,42 +1245,74 @@ 

 

 

 
-“The Environmental Effects of Economic Production: Evidence from Ecological Observations (previous title: Economic Production and Biodiversity in the United States)”
+AI and econ. growth
 
 
-1ef6aff67012a1750f88f631fddb346c
+Seth Benzell
 
 
-NA
+macroeconomics
 
 
-internal-NBER
+internal-from-syllabus-agenda-policy-database
 
 
-70
+80
+
+75
+
+80
 
 
 70
 
 
+NA
+

+AI and econ. growth
+
+Phil Trammel
+
+macroeconomics
+
+internal-from-syllabus-agenda-policy-database
+
+92
+
+97
+
 70
 
 
-75
+45
 
 
-4.0
+3.5
 
 

 
 

 

 

+

+
Next, look for systematic variation

 
    
 
  • By field and topic area of paper
    • 
 
  • By submission/selection route
    • 
 
  • By evaluation manager
    • 
 

+
… perhaps building a model of this. We are looking for systematic ‘biases and trends’, loosely speaking, to help us better understand how our evaluation system is working.

 


 
Relationship among the ratings (and predictions)

 
    
@@ -1288,7 +1331,7 @@ 
    
 

 2.3 Notes on sources and approaches

 

-
-

+

 

 
(Consult, e.g., repliCATS/Hanea and others work; meta-science and meta-analysis approaches)

 
aggrecat package

@@ -1311,7 +1354,7 @@ 

 

 

 

-
-

+

 

 
link

 
… we show how experts can be ranked based on their knowledge and their level of (un)certainty. By letting experts specify their knowledge in the form of a probability distribution, we can assess how accurately they can predict new data, and how appropriate their level of (un)certainty is. The expert’s specified probability distribution can be seen as a prior in a Bayesian statistical setting. We evaluate these priors by extending an existing prior-data (dis)agreement measure, the Data Agreement Criterion, and compare this approach to using Bayes factors to assess prior specification. We compare experts with each other and the data to evaluate their appropriateness. Using this method, new research questions can be asked and answered, for instance: Which expert predicts the new data best? Is there agreement between my experts and the data? Which experts’ representation is more valid or useful? Can we reach convergence between expert judgement and data? We provided an empirical example ranking (regional) directors of a large financial institution based on their predictions of turnover.

@@ -1329,7 +1372,7 @@ 

 

 

 

-
-

+

 

 
See Gsheet HERE, generated from an Elicit.org inquiry.

 

@@ -1385,7 +1428,10 @@ 

 
 

 
    
-
  1. More or less, the ones who report a level for ‘conf overall’, although some people did this for some but not others↩︎
    2. 
+
  2. Note this is only a first-pass; a more sophisticated approach may be warranted in future.↩︎
    3. 
+
  3. Consider: timing might be its own section or chapter; this is a major thing journals track, and we want to keep track of ourselves↩︎
    4. 
+
  4. We use “ub imp” (and “lb imp”) to denote the upper and lower bounds given by evaluators.↩︎
    5. 
+
  5. More or less, the ones who report a level for ‘conf overall’, although some people did this for some but not others↩︎
    6.