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Record W2998411926

Increasing Support for Victims of Sexual Assault through the Adoption of a Victim-Centered Approach to Police Investigations

2019· article· en· W2998411926 on OpenAlexaboutno aff
Kara Brooks

Bibliographic record

VenueScholarship@Western (Western University) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSexual assaultCriminologySexual violencePsychologyPoison controlHuman factors and ergonomicsSocial psychologyComputer securityMedical emergencyMedicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

Police services across North America have been criticized for their lack of support for victims throughout the investigative process, especially when investigating crimes involving sexual violence. The Problem of Practice addressed in this Organizational Improvement Plan (OIP) focuses on the lack of victim support throughout sexual assault investigations in a large police service in Canada. Taking a victim-centered approach to sexual assault investigations pushes the police organization to consider victims’ needs and rights ahead of strictly gathering information throughout a sexual assault investigation.\nA gap analysis identified two main areas of focus for this (OIP): the need for enhanced training and resources for police officers and the need for increased oversight and accountability mechanisms of those investigating sexual assaults. A plan for implementing a tiered training approach along with easily accessible online materials and resources for police officers aims to increase their knowledge surrounding victim-centered, trauma-informed approaches to supporting victims of sexual assault. A reinvestment of several officers throughout the organization provides a cost-effective approach to ensuring the oversight and accountability measures are in place to conduct appropriate, victim-centered, sexual assault investigations.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.131
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.160
GPT teacher head0.370
Teacher spread0.209 · 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 teacher head, 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
Published2019
Admission routes1
Has abstractyes

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