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Record W2973049423 · doi:10.1123/apaq.2019-0007

What’s in a Sport Class? The Classification Experiences of Paraswimmers

2019· article· en· W2973049423 on OpenAlexaff
Kirsti Van Dornick, Nancy Spencer-Cavaliere

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2019
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCertaintyClass (philosophy)Diversity (politics)PsychologyDignityCompetition (biology)Outcome (game theory)Process (computing)Social psychologyApplied psychologyPolitical scienceComputer scienceArtificial intelligenceEpistemologyMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the classification experiences (perspectives and reflections) of paraswimmers. Classification provides a structure for parasport, with the goal of reducing the impact of impairment on the outcome of competition. Guided by interpretive description, nine paraswimmers ranging in swimming experience and sport class were interviewed. Reflective notes were also collected. Transcribed interviews were analyzed inductively, followed by a deductive analysis using Nordenfelt's dignity framework. Three themes represent the findings: access, diversity, and (un)certainty. Despite several positive experiences, paraswimmers also discussed inconsistencies in the process leading them to question competition fairness and classification accuracy. These findings suggest that continued efforts to improve the classification system are required. In addition, paraswimmers and their allies (e.g., coaches) require more information about the classification process to better understand the outcomes and to effectively advocate for their needs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.329
Teacher spread0.299 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations32
Published2019
Admission routes1
Has abstractyes

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