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

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Justin Visser 2026-06-27 18:24:24 +02:00
parent 8e1964c0dd
commit dcab062f09
3 changed files with 8 additions and 8 deletions
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12
web/src/components/MethodPage.vue
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@ -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
ranges drawn from adjacent open-access literatures on misinformation spread, complex
forwarding, novelty, harm awareness, program effect) are illustrative values you can vary,
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

2
web/src/presets/deepfake-school.ts
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@ -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: