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Record W642693008 · doi:10.5040/9781718206625

Self-Efficacy in Sport

2008· book· en· W642693008 on OpenAlexaff
Deborah L. Feltz, Sandra E. Short, Philip Sullivan

Bibliographic record

VenueHuman Kinetics eBooks · 2008
Typebook
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsBrock University
Fundersnot available
KeywordsAthletesPsychologySport psychologyField (mathematics)Applied psychologySelf-efficacyMedical educationEngineering ethicsSocial psychologyMedicineEngineeringPhysical therapy

Abstract

fetched live from OpenAlex

<JATS1:p>This book provides a reference for sport psychology lecturers, students, coaches and other professionals with an interest in the field. Self-belief, known as 'self-efficacy' by sports psychologists is widely believed to be an essential component of sporting success. This book examines the nature of efficacy as it applies to sporting behaviour in coaches, athletes and teams. It is the first reference book entirely devoted to the concept of self-efficacy in sport. It provides readers with a basic background of research on the topic, as well as current and future trends; offers research-tested guidelines and recommendations for using efficacy techniques with individual athletes and teams; and, includes a comprehensive annotated bibliography of references on the topic.</JATS1:p>

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.006

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 head0.038
GPT teacher head0.319
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations350
Published2008
Admission routes1
Has abstractyes

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