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Record W2153412931 · doi:10.1177/0269758011422475

‘We are all vulnerable’

2012· article· en· W2153412931 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Review of Victimology · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAngerCriminologyVulnerability (computing)Social psychologyPsychologyPoison controlSuicide preventionHuman factors and ergonomicsComputer securityMedicineMedical emergency

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.740
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.068
GPT teacher head0.415
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it