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Record W2042830780 · doi:10.1260/174795409790291376

Female Athletes' Perceptions of a Coach's Speeches

2009· article· en· W2042830780 on OpenAlexaff
Carolynn Breakey, Martin I. Jones, Ceara-Tess Cunningham, Nicholas L. Holt

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2009
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyPerceptionAthletesSocial psychologyContent analysisSet (abstract data type)Applied psychologyTeam sportMedicinePhysical therapy

Abstract

fetched live from OpenAlex

The purpose of this case study was to examine female athletes' positive and negative perceptions of their coach's pre-game and intermission speeches. Members (n = 20) of a highly successful university women's hockey team were interviewed following two home stands. Researchers transcribed interviews verbatim and conducted an inductive content analysis. Positive features of the speeches were when the coach displayed genuine emotion, spoke in a short and meaningful way, and referred to a set of team values. Participants negatively perceived long and poorly timed speeches, instances when they disagreed with the coach, and when the coach omitted expected information or provided a new unexpected approach. Athletes consistently reported more positive perceptions of speeches than negative perceptions. In summary, the content (e.g., referring to team values) and the delivery (e.g., displaying genuine emotion) of speeches appeared to be closely connected.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.431
Teacher spread0.383 · 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

Citations21
Published2009
Admission routes1
Has abstractyes

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