Bibliographic record
Abstract
Ironically, while scholars and policy-makers have long referred to hate crime as a ‘message crime’, the assumption that those beyond the immediate victim are likewise intimidated by the violence has gone untested. Grounded in a recent study of the community impacts of hate crime, we offer some insights into these in terrorem effects of hate crime. We present here some of our qualitative findings. Interestingly, our findings suggest that, in many ways, awareness of violence directed toward another within an identifiable target group yields strikingly similar patterns of emotional and behavioural responses among vicarious victims. They, too, note a complex syndrome of reactions, including shock, anger, fear/vulnerability, inferiority, and a sense of the normativity of violence. And, like the proximal victim, the distal victims often engage in subsequent behavioural shifts, such as changing patterns of social interaction. On a positive note, there is also some evidence that these reactions can culminate not in withdrawal, but in the potential for community mobilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".