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Record W4253799697 · doi:10.1057/9781137378910_1

Introduction

2015· book-chapter· sv· W4253799697 on OpenAlexaboutno aff
Kaitlynn Mendes

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2015
Typebook-chapter
Languagesv
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClothingAngerQueen (butterfly)MythologyMedia studiesSexual assaultPsychologyCriminologySociologyHistoryLawPolitical scienceSocial psychologyMedicinePoison controlSuicide prevention

Abstract

fetched live from OpenAlex

In January 2011, Toronto Police Constable Michael Sanguinetti addressed a small group of York University students on campus safety. Prefaced by the statement ‘I’m told I’m not supposed to say this’ he went on to advise that, ‘women should avoid dressing like sluts in order not to be victimized’ (Kwan 2011). While his intention might have been to protect women, his comments that ‘slutty’ women attract sexual assault perpetuated the long-standing myth that victims are responsible, or somehow ‘are asking’ for the violence used against them. In response to PC Sanguinetti’s comments, Toronto residents Heather Jarvis and Sonya Barnett translated their anger at the ways women were slut-shamed and victim-blamed, into political activism. Creating a website and Facebook and Twitter accounts, the women invited the public to join them for a ‘SlutWalk’ to the Toronto Police Headquarters to vent their frustration. On 3 April 2011, the first SlutWalk set off from Queen’s Park in Toronto, attended by thousands. Although the organizers asked people to dress in their normal, everyday clothing to demonstrate the ways that sexual assault occurs no matter what women wear, a number of attendees showed up in ‘provocative’ attire to make a statement that no matter how they dress, they do not deserve to be assaulted.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.056
GPT teacher head0.308
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations1
Published2015
Admission routes1
Has abstractyes

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