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Record W4401725204 · doi:10.1123/iscj.2023-0086

A Nordic Ski Coach’s Learning Journey Towards Creating More Inclusive and Safer Sport

2024· article· en· W4401725204 on OpenAlexaff
Sara Kramers, Sophie Carrier-Laforte, Martin Camiré

Bibliographic record

VenueInternational Sport Coaching Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSAFERBusinessAeronauticsComputer scienceComputer securityEngineering

Abstract

fetched live from OpenAlex

Competitive youth sport coaches who aim to foster inclusive and safer sport often face challenges from their peers, their organisations, and the sociocultural systems in their contexts. A personal learning coach may support coaches’ critical awareness, reflection, and readiness for working towards changing their youth sport contexts. This study details a 15-month collaboration, as Sara acted as a personal learning coach to support Sophie’s critical praxis as they reflected on social issues and experienced shifts in their coaching towards creating more inclusive and safer sport. Grounded in a narrative inquiry methodology, two virtual interviews and 11 virtual meetings occurred. Sara and Sophie also shared reflections through messages and voice notes and one in-person meeting during one of Sophie’s training sessions. Through time-hopping snapshot vignettes, Sophie’s learning journey is presented as they attempt to figure out what to fight for, grow through discomforts and unknowns, and experience progress. Sophie believed that their “mind shifted” towards becoming a “better coach” throughout the collaboration, developing their critical consciousness to change oppressive social conditions in sport. By sharing insights from the collaboration, the study provides vivid examples of the steps coaches and sport stakeholders can take to become more confident in enacting positive change in sport.

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 categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.468
Teacher spread0.423 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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