Political Pugilists: Recuperative Gender Strategies in Canadian Electoral Politics
Bibliographic record
Abstract
This paper offers the concept recuperative gender strategies to describe how political leaders work to restore their public gender identities. The author examines a charity-boxing match between two Canadian politicians, Justin Trudeau and Patrick Brazeau. Trudeau is the current leader of the Liberal Party of Canada and son of former Prime Minister, Pierre Trudeau. Brazeau was a Conservative Senator. Through a discourse analysis of 222 national newspaper articles published on the match, this paper chronicles Justin Trudeau's transition from "precariously masculine" to "sufficiently masculine" and discusses the significance of this transformation for Trudeau's suitability for Liberal Party leadership. Cet article propose le concept de stratégies de récupération des sexes pour décrire et expliquer comment des dirigeants politiques travaillent à rétablir leurs identités sexuelles publiques. J'analyse la couverture médiatique du combat de boxe caritatif datant de mars 2012 et opposant deux politiciens canadiens : Justin Trudeau, le chef du Parti libéral du Canada, et Patrick Brazeau, un sénateur conservateur. En m'appuyant sur une analyse de discours de 222 articles de journaux nationaux publiés au sujet de ce combat, je détaille la transition de Justin Trudeau d'une forme de masculinité « précaire » à une « masculinité suffisante », et je discute de l'importance de cette transformation pour l'aptitude perçue de Trudeau comme chef du Parti libéral.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.029 | 0.021 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".