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Record W2002765762 · doi:10.1177/1043986208315483

Interpersonal Violence Against Women

2008· article· en· W2002765762 on OpenAlexaff
Martin D. Schwartz, Walter S. DeKeseredy

Bibliographic record

VenueJournal of Contemporary Criminal Justice · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsOutrageStalkingCriminologyInterpersonal communicationInterpersonal violencePublic relationsSocial psychologyPolitical sciencePsychologyPoison controlLawSuicide preventionMedicine

Abstract

fetched live from OpenAlex

Attempting to solve the problem of interpersonal violence by dealing with the private problems of individuals is a strategy doomed to failure. With high-level social forces combining to facilitate rape, abuse, and stalking, programs to end these problems must be painted with broad strokes. Male peer support is an important aspect of society giving permission to men to assault women or to encourage or ignore others who do so. Programs such as bystander education that encourage male leaders to speak out are essential. Schools and governments must put more money into education programs to protect youth. American society was outraged when a professional football player was accused of mistreating and killing fighting dogs. Hollywood, meanwhile, virtually cannot portray the mistreatment of animals. We need to move toward a society where the same level of outrage accompanies acts of interpersonal violence against women.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.002

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 head0.084
GPT teacher head0.344
Teacher spread0.260 · 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

Citations48
Published2008
Admission routes1
Has abstractyes

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