Prototypes of Hate and Expectations of the Model Victim
Bibliographic record
Abstract
This research explored the content of hate crime prototypes in a North American context, with particular attention to how such prototypes might influence blame attributions. In Study 1a, participants were recruited from a blended sample of universities ( n = 110) and community members ( n = 102) and asked to report their thoughts about typical hate crime offenses, victims, and offenders. These open-ended responses were coded, and common themes were identified. In Study 1b, a new group of participants ( n = 290) were presented with these themes and asked to rate each for their characteristics of hate crimes. Studies 1a and 1b confirmed the presence of a clear prototype of hate crimes, such that (a) perpetrators were believed to be lower status White men with clear expressions of bias, (b) hate crime offenses were believed to be acts of interpersonal violence accompanied by slurs or verbal abuse, and (c) hate crime victims were thought to be members of a marginalized group who remain passive during the offense. Study 2 explored the consequences of victim prototypes on assessments of victim blame. Participants ( n = 296) were recruited from York University and presented with a case vignette that varied the prototypicality of a victim of hate, depicting him as either Black or White and either passive, verbally responsive, or physically confrontational in the context of an assault. Participants showed greatest sympathy for the Black victim who passively ignored verbal harassment but increasingly assigned blame when the Black victim spoke or reacted physically. When the victim was White, participants showed little variation in their assessment of blame as a function of the victim’s behavior. These results suggest that Black victims are subjected to greater behavioral scrutiny than White victims and that sympathy for victims of hate may be contingent on their passivity in the face of harassment.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".