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Examining World Rugby's Transgender Ban and the Perspectives of Cisgender Women Who Play Rugby in England, Canada and Australia

2022· book-chapter· en· W4309452915 on OpenAlexaboutno aff
Richard Pringle, Erik Denison

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsTransgenderHarassmentCoachingEliteHarmPolitical scienceFootballAthletesGender studiesEthnic groupCriminologyPsychologySociologyLawMedicinePolitics

Abstract

fetched live from OpenAlex

Abstract This chapter critically examines the unprecedented 2020 decision by World Rugby's (WR) primarily male leadership (92% of board members) to ban transgender (trans) women from playing women's rugby union. We examined the process that was followed and found a lack of consultation with those directly impacted: women. To address this critical gap in the policy development process we conducted interviews and focus groups with cisgender female rugby players (junior to elite) of mixed ethnic backgrounds living in England, Canada and Australia. This was done with the support of rugby governing bodies and professional rugby teams. We found no support for WR's blanket ban. Rugby players felt the policy was a contradiction of rugby's claims it is a ‘game for all’. The minority of players with safety concerns supported exclusion on a case-by-case basis, with exclusion justified in a small number of narrowly defined circumstances (e.g. elite male players who transitioned recently). Importantly, the women and girls questioned why rugby's leaders had chosen to focus their energy on ‘protecting’ them from trans athletes but had ignored serious problems which cause them direct harm, such as a lack of funding, pervasive sexist and homophobic behaviour, sexual harassment, and substandard coaching and training facilities (relative to men). Our findings are consistent with and they support the position of women's sports organizations which have called on WR's male leaders to discard their blanket ban and undertake a rigorous, science-driven, collaborative policy development process.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.051
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0300.013
Scholarly communication0.0080.002
Open science0.0020.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.045
GPT teacher head0.266
Teacher spread0.221 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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