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Record W3114261285 · doi:10.3138/anth-2020-0007

The Medicalization of Workplace Sexual Violence on Canadian University Campuses in the #MeToo Era

2020· article· en· W3114261285 on OpenAlexaffvenueabout
Alexandria Petit-Thorne

Bibliographic record

VenueAnthropologica · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsYork University
Fundersnot available
KeywordsHarassmentMedicalizationRedressSexual violenceCriminologyWorkplace violenceStalkingSexual abusePoison controlPsychologySociologyPolitical scienceSuicide preventionPsychiatryMedicineSocial psychologyLawMedical emergency

Abstract

fetched live from OpenAlex

The #MeToo movement has met institutional barriers to addressing workplace sexual violence in practice. Structural barriers to reporting workplace sexual harassment at Canadian universities are maintained through sexual violence policies that reduce sexual violence to physical assault. Institutional focus on physical forms of sexual violence can be considered the product of medicalization, which allows sexual violence to be conceptualised solely around assault of the body proper. This has limited the legal, criminal and medical responses available to survivors by minimising forms of sexual violence that do not involve physical contact – like sexual harassment and stalking – but have significant impacts on survivors’ lives. In this vein, workplace sexual violence policies put focus on physical assault and limit the scope of the continuum of sexual violence in practice, solidifying barriers to the reporting, investigation and redress of workplace harassment. This article examines the effects of sexual violence medicalization in practice through an ethnographic exploration of reporting workplace harassment.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.326
Teacher spread0.279 · 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 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

Citations4
Published2020
Admission routes3
Has abstractyes

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