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Record W2090619464 · doi:10.1080/17511320802222008

Tackling<i>Murderball</i>: Masculinity, Disability and the Big Screen

2008· article· en· W2090619464 on OpenAlexaboutno aff
Michael Gard, Hayley Fitzgerald

Bibliographic record

VenueSport Ethics and Philosophy · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityWheelchairRivalryValue (mathematics)Subject (documents)AthletesIntellectual disabilityGender studiesDisabled peoplePoliticsPsychologyWork (physics)SociologyPolitical scienceApplied psychologyMedicineLawEngineering

Abstract

fetched live from OpenAlex

The sport of wheelchair rugby is the subject of a recent film Murderball, which tells the story of the apparently intense rivalry between the Canadian and United States men's teams. In part, the story is told through the lives of some of the game's leading players and coaches. Murderball deals with a series of ethical and political questions concerned with conceptions of disability, articulations of sporting bodies, and the value attached to sporting performance. In this paper we offer a critique of Murderball and explore a number of themes including: (1) What can disabled bodies do?; (2) This is not the Special Olympics; and (3) ‘Hot’ and disabled. We conclude that these themes offer us new intellectual challenges for thinking about the physical education experiences of young disabled people and progression in disability sport. Indeed, we argue that Murderball moves disability issues into new intellectual terrain, thus increasing the ways in which people who work with young people and sport might need to take account of disability.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0130.035
Scholarly communication0.0080.003
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.089
GPT teacher head0.342
Teacher spread0.253 · 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 designTheoretical or conceptual
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

Citations24
Published2008
Admission routes1
Has abstractyes

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