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Record W2767472969 · doi:10.1177/0093854817738279

Differential Effects of Gender on Canadian Police Officers’ Perceptions of Stalking

2017· article· en· W2767472969 on OpenAlexafffundabout
Heather Finnegan, Patti A. Timmons Fritz, Barry Horrobin

Bibliographic record

VenueCriminal Justice and Behavior · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsStalkingSeriousnessConvictionHarassmentPsychologyCriminologyLaw enforcementSocial psychologyHarmPrisonPoison controlPerceptionIntervention (counseling)JuryMedicinePolitical sciencePsychiatryLawMedical emergency

Abstract

fetched live from OpenAlex

Stalking (also known as “criminal harassment” in Canada) is broadly defined as repeated contact with another individual that elicits fear. By manipulating the details of an actual stalking case, the present study examined the role of actor sex (man–woman [M-W], woman–man [W-M]) on perceptions of stalking in a sample of local police officers. Consistent with previous research, officers who read the M-W case anticipated more physical, emotional, psychological, and economic harm, as well as greater likelihood of a prison sentence by judges, than officers who read the W-M case. Actor sex did not influence officers’ perceptions of seriousness, likelihood of a jury conviction, or identification of criminal harassment. The findings may be used to develop intervention programs aimed at educating law enforcement, social support workers, and community agencies to ensure appropriate protection and treatment of individuals stalked by former partners.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.053
GPT teacher head0.357
Teacher spread0.304 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2017
Admission routes3
Has abstractyes

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