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Record W7015923896

Trauma Unspoken: Canadian Queer Women Politicians’ Experiences of Violence

2023· article· en· W7015923896 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsQueerHarassmentPoliticsLesbianPower (physics)VictorySubject (documents)Face (sociological concept)
DOInot available

Abstract

fetched live from OpenAlex

At the intersection of misogyny and heteronormativity, queer women in Canada face significant systemic barriers in pursuing political leadership, including experiences of harassment and violence. In transgressing gendered leadership norms in the high-stakes and high-visibility terrain of electoral politics, queer women are subject to disproportionate surveillance and discipline under the regulatory power of the heterosexual matrix. In taking up a recent quantitative study by the LGBTQ Victory Institute as well as media coverage of Kathleen Wynne’s leadership as the first openly lesbian and LGBTQ-identifying premier in Canada, this paper argues that in order to meaningfully support queer women to enter the field of electoral politics in Canada, we must move beyond encouraging representation and instead consider how violence against queer women in politics enforces systemic exclusion. This paper offers that understanding the trauma experienced by queer women in politics – both as candidates and elected leaders – is vital for illuminating the structural powers that perpetuate this violence and dismantling the systems that reproduce this 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)0.002
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.426

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0570.018
Scholarly communication0.0110.003
Open science0.0020.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0110.001

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 head0.111
GPT teacher head0.351
Teacher spread0.240 · 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
Published2023
Admission routes1
Has abstractyes

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