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Record W2617682956

Exploring the Experiences of Elite Male Hockey Players

2017· dissertation· en· W2617682956 on OpenAlexaboutno aff
Matt Norris

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsEliteIce hockeyField hockeyPsychologyAeronauticsPolitical scienceEngineeringPhysical medicine and rehabilitationFootballMedicinePolitics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the sport experiences of six U Sports (Canadian Interuniversity Sport) male hockey players (three past CHL players and three past NCAA Division I players) through one-on-one interviews with them. The focus was the exploration of how parents, coaches, and peers had influenced current U Sports hockey players’ overall well-being in the transition through the CHL (Canadian Hockey League) or NCAA (National Collegiate Athletic Association). This study included male participants only, because the CHL, one of the two development leagues investigated, is for males only. Interviews revealed meaningful hockey experiences within the CHL and NCAA, in climates that affected players' motivation, expectations, and goals. The key findings of this study arose from the analysis of these interviews: (1) coaches need to have a better knowledge of the ‘psychology of performance’ for their athletes; (2) coaches should provide feedback in a more ethical way regardless of competition level; and (3) all social agents, including the athletes themselves, need to create informed and appropriate expectations to avoid realizations that can result in negative outcomes to well-being. Future research should be expanded by broadening the range of interview questions, diversifying the participant pool, and targetting policies in addition to practices.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.392
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.245
Teacher spread0.212 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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