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Record W4414981675 · doi:10.1080/13573322.2025.2564247

On the topic of education in a corresponsive sport science

2025· article· en· W4414981675 on OpenAlexaff
Carl T. Woods, Véronique Richard, Martin Camiré, John van der Kamp

Bibliographic record

VenueSport Education and Society · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSports sciencePhysical educationScience educationTeaching methodHigher educationQualitative research

Abstract

fetched live from OpenAlex

If education is the way society produces its future, then what future lies ahead for the sport sciences? Our aim, here, is to think through this question across three interwoven sections. In the first, we consider a future of reproduction through transmission. This future is built upon a dominant, linear, and unidirectional educative model that assumes the instillation of authorised, inter-generationally transmitted, second-hand information. Its goal is to bring a younger generation to a predetermined point of ‘being’ a sport scientist. Yet, despite its dominance, this model brings with it a troubling question: what growth could there be beyond the confines of that which was transmitted by those gone before? In response, section two thinks through an alternative: a futurity of transformation through exposure. This futurity commits to a processual, open-ended and dialogical educative approach. By encouraging people to open-up to others, attuning to their doings while developing the capacity to respond, it promises not an arrival at ends determined from the start, but the sustenance of a correspondence that flows along in possibility of what could become. In section three, we advocate for a futurity of transformation through exposure in the sport sciences by thinking through three sensibilities underpinning its educative promise: (i) response-ability; (ii) differentiation; (iii) curiosity.

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)0.001
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 score0.707
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.483
Teacher spread0.450 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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