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Record W4294335686 · doi:10.32920/20808769.v1

In Our Own Words: Towards a Survivor-Informed Response to Sexual Harm

2022· preprint· en· W4294335686 on OpenAlexaffabout
Jennifer Good

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHarmLaw enforcementConvictionEnforcementCriminologyPolitical scienceLegitimacyEconomic JusticeLawPsychologyPolitics

Abstract

fetched live from OpenAlex

As a result of both the Black Lives Matter movement, as well as the ongoing violence inflicted upon Black individuals and communities by law enforcement in Canada and the United States, the prospect of abolishing or defunding law enforcement has entered public discourse as a tangible policy option. One common comment that upholds the legitimacy of law enforcement amidst the demand they be abolished is the question of interpersonal harm: without the police, who or what will protect survivors of harm? Given that only 3 in 1000 instances of sexual harm result in a conviction in Canada, law enforcement is actually a policy response to sexual harm that is underutilized and limited in its capacity to provide survivors with access to due process, justice, safety and healing following sexual harm. This qualitative inquiry seeks to amplify the perspectives of survivors themselves regarding their perceptions of and experiences with law enforcement, as well as what policy responses would have constituted adequate support following their experience(s) of sexual harm.

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)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.024
Scholarly communication0.0100.011
Open science0.0020.007
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0130.003

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 head0.055
GPT teacher head0.396
Teacher spread0.341 · 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 designQualitative
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
Published2022
Admission routes2
Has abstractyes

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