Differentiating between direct and indirect hate crime: Results from Poland
Bibliographic record
Abstract
Inspired by individual-level research on direct and indirect as well as reactive and proactive aggression, this article proposes to differentiate direct and indirect types of hate crime. We use the largest hate crime database in Poland (N = 3,153 incidents) to analyze: (1) temporal trends in the relative prevalence of two types of hate crime; (2) the involvement of hate group-affiliated and non-hate group-affiliated perpetrators; and (3) the targeting of victims that are perceived to pose more of a symbolic (vs. more of a realistic threat) to the majority group. Results indicate that direct hate crime was more likely than indirect hate crime to be perpetrated by members and affiliates of hate groups, was more likely to target outgroups seen as posing symbolic rather than realistic threat to the majority group, and was also positively related to societal levels of negative intergroup attitudes and negatively related to unemployment. The findings also show that the two types of hate crime are differently predicted by factors indicative of the social and political climate of the country (e.g., unemployment, political preferences, xenophobia). Although the results were only obtained in one cultural context and will benefit from further validation, they provide very promising initial evidence for the predictive utility of distinguishing direct and indirect hate-crime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".