Information alone might not be enough: The limited impact of exposure to factual information about historical atrocities
Bibliographic record
Abstract
Informing people about historical atrocities and injustice is considered critical for sustaining democracies and preventing similar atrocities in the future. Yet, what remains unknown is whether exposure to factual information about ingroups' historical injustices, such as genocide, slavery, or colonial crimes, leads to increased willingness to address those injustices? In the first study to systematically assess the impact of such exposure in five countries (Canada, France, Germany, Spain, United States), using large samples (n> 1500 per country) and a comprehensive battery of outcomes, we find limited impact of exposure to factual information. Participants in the experimental condition reported increased acknowledgment of the injustice and intentions to dismantle it in some but not all countries. Across all countries, we find that exposure led to self-reported learning, which predicted all measured outcomes. These findings suggest that whilst factual information is important, other ingredients are needed to facilitate broader dismantling of past injustice.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".