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Record W4400898303 · doi:10.55016/ojs/sppp.v17i1.78867

Disrupting Trajectories Leading to Domestic Violence

2024· article· en· W4400898303 on OpenAlexaffabout
Lana Wells, Ken Fyie, Ron Kneebone, Stephanie Montesanti, Casey Boodt, Rebecca Davidson

Bibliographic record

VenueThe School of Public Policy Publications · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsDomestic violencePsychologyMedical emergencyHuman factors and ergonomicsPoison controlMedicine

Abstract

fetched live from OpenAlex

Research into male-on-female domestic violence traditionally focuses on its after-effects, with an emphasis on how victims can keep themselves safe or on the men who have been criminally charged in such incidents. This approach puts the responsibility on the victim to try and protect herself while offering support to the perpetrator only after the violence has occurred to prevent recidivism. This policy brief takes a different approach to understanding points of intervention that might prevent domestic violence from occurring in the first place. Using a robust 10-year dataset supplied by Calgary Police Service, the authors explored a trajectory of criminal behaviour and police interactions prior to an eventual charge for a criminal act involving domestic violence in 2019. While preliminary, the data analysis reported in this brief finds a distinct trajectory of increased criminal behaviour among male perpetrators leading up to a charge in 2019. In fact, the data shows a rising number of police interventions relatedto complaints involving possible acts of domestic violence during that 10-year period. Very few men in this sample were unknown to police prior to the charge in 2019. Domestic violence frequently makes headlines, and when femicide is committed, it is often accompanied by announcements of public vigils to be held for the victimized woman along with demands for an end to intimate partner violence. But rarely is the question raised, why do men continue to be the major perpetrators of this terrible violent act? And if there is always a passion and commitment to provide support to victims, where is the same passion and commitment to developing policies and strategies to work with men at risk of perpetrating violence and before they commit the offence of domestic violence? The approach of examining male perpetration trajectories analyzed in this policy brief, can help inform legislation, policies, and programs that can not only stop male violence before it starts, but subsequently reduce the suffering of women and their families.

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)0.006
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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.057
GPT teacher head0.410
Teacher spread0.352 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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