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Record W3036253028 · doi:10.1123/ssj.2019-0146

Too Many Chairs: Spatiality and Disability in Integrated Sporting Spaces

2020· article· en· W3036253028 on OpenAlexaff
Nancy Quinn, Laura Misener, P. David Howe

Bibliographic record

VenueSociology of Sport Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsWestern University
Fundersnot available
KeywordsInsiderPerspective (graphical)EthnographySociologyForegroundingSpace (punctuation)VignetteAbleismAthletesEmbodied cognitionThematic analysisGender studiesMedia studiesPsychologySocial scienceSocial psychologyEpistemologyAnthropologyVisual artsQualitative researchLinguisticsArtMedicine

Abstract

fetched live from OpenAlex

The research examined spatiality of The Village during the Commonwealth Games XXI. Central to the research is the perspective of the parasport athlete. By foregrounding this perspective, new understandings of the geography of sporting spaces become possible. The integrated nature of the Games establishes The Village as a significant space to consider spatiality and disability. Ethnographic methodology was utilized. The first author, a veteran of many Paralympic Games, brought an “insider” perspective. Thematic analysis was conducted, and three themes, such as language informs space, hypervisibility of the body, and indoor versus outdoor spaces are presented as an ethnographic vignette. Inaccessible construction and hypervisibility of the body in certain spaces impacted athlete experience. The Village Pub and pools were examples of inhospitable spaces for athletes. The language of Games personnel significantly affected athlete participation in Village life.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.012
Scholarly communication0.0040.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.106
GPT teacher head0.471
Teacher spread0.365 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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