Harnessing dehumanization theory, modern media, and an intervention tournament to reduce support for retributive war crimes
Bibliographic record
Abstract
We demonstrate how psychological scientists can curate rich-yet-accessible media to intervene on conflict-escalating attitudes during the earliest stages of violent conflicts. Although wartime atrocities all-too-often ignite destructive cycles of tit-for-tat war crimes, powerful third parties can de-escalate the bloodshed. Therefore, following Russia's illegal invasion of Ukraine, we aimed to reduce Americans' support for committing retributive war crimes against Russian soldiers. To intervene during the earliest stages of the invasion, we drew on theories of dehumanization and “parasocial” intergroup contact to curate publicly available media expected to humanize Russian soldiers. We then identified the most effective materials by simultaneously evaluating all of them with an intervention tournament. This allowed us to quickly implement a psychological intervention that reliably reduced support for war crimes during the first days of a momentous land war. Our work provides a practical, result-driven model for developing psychological interventions with the potential to de-escalate incipient conflicts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".