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Record W4391174918 · doi:10.56367/oag-041-11234

Empowerment through education: Sexual assault resistance programs for girls and young women

2024· article· en· W4391174918 on OpenAlexaff
Charlene Y. Senn, Sara Crann

Bibliographic record

VenueOpen Access Government · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSexual assaultResistance (ecology)EmpowermentPsychologyDevelopmental psychologyCriminologyHuman factors and ergonomicsPoison controlMedicinePolitical scienceMedical emergency

Abstract

fetched live from OpenAlex

Empowerment through education: Sexual assault resistance programs for girls and young women Charlene Y. Senn and Sara E. Crann from the University of Windsor discuss the importance of sexual assault resistance programs in equipping girls and young women with the knowledge and skills to reduce the risk of sexual assault. Sexual assault is a form of gender-based violence. While anyone can be a victim, research consistently finds that victims are most often girls and women (~85%), and 90% of perpetrators are boys and men. Research also shows that it is girls and young women between 14-24 years old who are at the highest risk for sexual assault. Despite over 50 years of high-quality research on sexual assault and widespread attention to the issue through movements like #MeToo, we have seen little progress in reducing sexual assault on a broad scale. Ending sexual assault requires a comprehensive strategy that includes:

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0080.003
Open science0.0020.001
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 head0.104
GPT teacher head0.502
Teacher spread0.397 · 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.

Study designNot applicable
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 routes1
Has abstractyes

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