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Record W2890142906 · doi:10.11575/prism/31408

State of the Science Brief: Programmatic Approaches to Sexual Violence Prevention and Risk Reduction in Post-Secondary Settings

2017· article· en· W2890142906 on OpenAlexaboutno aff
Deinera Exner‐Cortens, Lana Wells

Bibliographic record

VenueOpen MIND · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Sexual violenceReduction (mathematics)Sexual abusePoison controlPolitical scienceSuicide preventionCriminologyPsychologyMedicineMedical emergencyComputer science

Abstract

fetched live from OpenAlex

The authors of this paper and Shift: The Project to End Domestic Violence believe that sexual assault is NEVER the victim’s fault. Further, we wish to emphasize that research on risk reduction should not be taken to imply that victims are responsible for protecting themselves from assault. For too long, survivors have been blamed by individuals and systems for sexual assault, and thus we must whole-heartedly resist any discourse that blames and shames victims. However, we have found through the research that effective rape resistance programs within a specific context may impact the experience of sexual violence, and so we chose to present that research here. To that end, readers should only consider the presented research and findings within the context of the post-secondary environment, as this is the setting where all reported research was conducted: the post-secondary environment is a unique setting and we thus discourage the generalization of findings to other settings and age groups. It is our hope that this report leads to a robust discussion of these findings, and what they mean for sexual violence prevention in Alberta.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.101
GPT teacher head0.363
Teacher spread0.262 · 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 designOther design
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

Citations2
Published2017
Admission routes1
Has abstractyes

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