drop the low/medium/high sensitivity-range framing: parameters are illustrative values, the robust claim is the ordering

README.md

@@ -46,7 +46,7 @@ is the point of the tool.
   rate, raised by how novel or shocking the fake is, lowered by the year
   group's ambient harm awareness. Per-edge random draws are shared across all
   scenarios, so strategies are compared in the same world. Because
-  deepfake-specific data are scarce, the parameters are sensitivity ranges
+  deepfake-specific data are scarce, the parameters are illustrative values
   rather than fitted values, drawn from the project team's literature review.
 - **Education lever**: the program cuts an educated student's forwarding
   probability by its effect size (a strong but imperfect reduction, not a

web/src/components/MethodPage.vue

@@ -28,7 +28,7 @@ const parameterSources: ParameterSource[] = [
   {
     name: 'Baseline forwarding',
     status: 'live',
-    role: 'Each student’s base chance of forwarding the fake along a friendship after seeing it. Treated as a low / medium / high sensitivity range, not a fitted fact, because deepfake-specific adolescent diffusion data are scarce.',
+    role: 'Each student’s base chance of forwarding the fake along a friendship after seeing it. Treated as an illustrative, uncertain value, not a fitted fact, because deepfake-specific adolescent diffusion data are scarce.',
     sources: [
       'Kim et al. (2023), comprehensive sexuality education meta-analysis',
       'Brigham et al. (2024), perceptions of AI-generated non-consensual imagery',
@@ -54,7 +54,7 @@ const parameterSources: ParameterSource[] = [
   {
     name: 'Program effect (the education lever)',
     status: 'live',
-    role: 'Applies education as a strong but imperfect reduction in an educated student’s forwarding probability. Explored as a sensitivity range, because intervention effect sizes vary and no deepfake-specific causal estimate exists.',
+    role: 'Applies education as a strong but imperfect reduction in an educated student’s forwarding probability. Treated as illustrative and uncertain, because intervention effect sizes vary and no deepfake-specific causal estimate exists.',
     sources: [
       'Kim et al. (2023), comprehensive sexuality education meta-analysis',
       'Kamaruddin et al. (2023), cyberbullying intervention meta-analysis',
@@ -214,7 +214,7 @@ const references: Reference[] = [
       </p>
       <p class="disclaimer" role="note">
         <strong>Illustrative, not validated.</strong> spreadlab is a planning and discussion aid,
-        not a prediction. Its parameters are explored as sensitivity ranges drawn from adjacent
+        not a prediction. Its parameters are illustrative values drawn from adjacent
         literatures, not fitted to real-school data, so its numbers must not be read as forecasts
         about any real school, platform, or incident.
       </p>
@@ -323,12 +323,12 @@ const references: Reference[] = [
       </div>
 
       <div class="block">
-        <h3>Details: parameters as sensitivity ranges, not fitted values</h3>
+        <h3>Details: parameters are illustrative, not fitted values</h3>
         <p>
           Because direct empirical data on adolescent sexualized deepfake diffusion are still
           scarce, the model does not bake in point estimates. The curated levers (baseline
-          forwarding, novelty, harm awareness, program effect) are explored as low / medium / high
+          forwarding, novelty, harm awareness, program effect) are illustrative values you can vary,
-          ranges drawn from adjacent open-access literatures on misinformation spread, complex
+          drawn from adjacent open-access literatures on misinformation spread, complex
           contagion, peer aggression, cyberbullying interventions, and comprehensive sexuality
           education. The remaining literature parameters are documented and reserved for the
           calibrated instrument the grant would fund. The list below maps each parameter to its

web/src/presets/deepfake-school.ts

@@ -17,7 +17,7 @@ export const deepfakeSchoolPreset: StudyPreset = {
     'fake along each friendship that rises with how novel it is and falls ' +
     'with the ambient harm awareness, and an education program that strongly ' +
     'but imperfectly reduces forwarding for educated students. Its parameters ' +
-    'are illustrative sensitivity ranges, not fitted to data, so it is not a ' +
+    'are illustrative values, not fitted to data, so it is not a ' +
     'validated prediction of any real school. Use it to build intuition about ' +
     'who to educate, not to forecast outcomes.',
   readingCaption: