From bcd247edd590ad3cb322100a8c88b98b176c2e40 Mon Sep 17 00:00:00 2001
From: colin_f_gergaghty <colin.geraghty@gmail.com>
Date: Sun, 8 Dec 2024 18:49:12 -0500
Subject: [PATCH] Removed second visualization

---
 fisher.html | 46 +++++++++-------------------------------------
 1 file changed, 9 insertions(+), 37 deletions(-)

diff --git a/fisher.html b/fisher.html
index b00caa2..3ddc26a 100644
--- a/fisher.html
+++ b/fisher.html
@@ -29,43 +29,20 @@ <h1>Fisher Doubles Down</h1>
         <!-- Embed Fisher Visualization -->
         <section id="ethnic-visualization">
             <p>
-                Explore the interactive visualization directly: Adjust the weights assigned to variables like IQ, moral character, and crime statistics to explore how hierarchies might emerge.
-            </p>
-            <iframe 
-    src="https://colingeraghty.pythonanywhere.com/fisher_weight1" 
-    title="Fisher Visualization" 
-    width="100%" 
-    height="600px" 
-    style="border: none;">
-</iframe>
-                How to interact with the visualization:
+                Step into Fisher’s mindset: Adjust the weights assigned to variables like IQ, moral character, and crime statistics to explore how hierarchies might emerge.
             </p>
             <ul>
-                <li>Click on the variable names at the top of the visualization to include or exclude them from the analysis. Think about which variables are prioritized in contemporary narratives of "success."</li>
+                <li>Click on the variable names at the top of the visualization to include or exclude them from the analysis.</li>
                 <li>Adjust the sliders to assign weights to each variable. Observe how changing these weights shifts the bar chart and alters the perceived rankings of groups.</li>
                 <li>Reflect on the narratives created by your adjustments. Which groups appear to "succeed" under certain weightings? Which are marginalized? How does this depend on the values embedded in the variables?</li>
             </ul>
-        </section>
-
-        <!-- Modern Visualization -->
-        <section id="modern-visualization">
-            <p>
-                While the overtly biased metrics of Fisher's time have faded, the mechanisms remain. Today, variables like educational attainment, health outcomes, and income disparity are used to measure societal progress. These metrics may seem impartial, but they are often laden with the values of those who define them.
-            </p>
             <iframe 
-    src="https://colingeraghty.pythonanywhere.com/fisher_weight2" 
-    title="Modern Visualization" 
-    width="100%" 
-    height="600px" 
-    style="border: none;">
-</iframe>
-                How to interact with the visualization:
-            </p>
-            <ul>
-                <li>Click on the variable names at the top of the visualization to include or exclude them from the analysis. Think about which variables are prioritized in contemporary narratives of "success."</li>
-                <li>Adjust the sliders to assign weights to each variable. Observe how changing these weights shifts the bar chart and alters the perceived rankings of groups.</li>
-                <li>Reflect on the narratives created by your adjustments. Which groups appear to "succeed" under certain weightings? Which are marginalized? How does this depend on the values embedded in the variables?</li>
-            </ul>
+                src="https://colingeraghty.pythonanywhere.com/fisher_weight1" 
+                title="Fisher Visualization" 
+                width="100%" 
+                height="600px" 
+                style="border: none;">
+            </iframe>
         </section>
 
         <!-- Conclusion -->
@@ -73,12 +50,6 @@ <h1>Fisher Doubles Down</h1>
             <p>
                 These exercises reveal how statistical tools, designed to understand the world, can easily divide it. From Fisher’s justification of resource allocation to today’s efforts to measure societal progress, the ability to assign weights and create hierarchies reflects subjective values rather than objective truths.
             </p>
-            <p>
-                The persistence of IQ as a central metric exemplifies this issue. While IQ measures something quantifiable, it remains deeply entwined with assumptions about intelligence and human worth. Fisher’s belief in its importance, like his focus on other traits, was shaped by a worldview that sought to categorize and control. Even today, IQ’s prominence in debates about education, employment, and societal potential reflects these enduring biases.
-            </p>
-            <p>
-                As we turn to IQ in the next section, we confront its role as a "constant variable" across decades of data analysis. By examining how it has been used to define human potential and societal value, we can better understand the subjective choices that continue to shape modern metrics. <a href="the_constant_variable.html">Continue reading: The Constant Variable</a>
-            </p>
         </section>
     </div>
 
@@ -87,3 +58,4 @@ <h1>Fisher Doubles Down</h1>
     </footer>
 </body>
 </html>
+