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Record W4313274991 · doi:10.1123/iscj.2022-0042

Reviewing Original Research Articles Published in the International Sport Coaching Journal

2022· article· en· W4313274991 on OpenAlexaff
Katherine E. Hirsch, Todd M. Loughead, Gordon A. Bloom, Wade Gilbert

Bibliographic record

VenueInternational Sport Coaching Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsMcGill UniversityUniversity of Windsor
Fundersnot available
KeywordsCoachingScope (computer science)Variety (cybernetics)Diversity (politics)Empirical researchPsychologyData collectionAthletesQualitative researchApplied psychologyMedical educationPolitical scienceSocial scienceSociologyMedicineComputer science

Abstract

fetched live from OpenAlex

The purpose of this commentary is to provide a broad overview of the empirical research-based articles published in the International Sport Coaching Journal from its inception in 2014 through 2020. Data from 101 publications were collected and analyzed using Arksey and O’Malley’s six-stage framework for conducting scoping reviews. Data were extracted on the size and scope of research, populations and perspectives studied, and methodologies and data collection methods used. The results show that empirical research publications grew more prominent over time (i.e., 24.0% of 2014 publications vs. 58.1% of 2020 publications) compared with other publication types. The most commonly researched topics included coach development and coach behaviors. The participants most studied were male coaches, performance sport coaches, and adult sport coaches, featuring primarily European and North American coaches. The majority of studies used a qualitative methodology with the most common research designs being phenomenological and case studies. A variety of data collection methods were used that involved one-on-one interviews and questionnaires. Several recommendations are advanced to stakeholders, including strategies to promote racial and gender diversity and to collect and report demographic data on race and coaching experience.

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)0.027
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 score0.643
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0040.000
Research integrity0.0000.007
Insufficient payload (model declined to judge)0.0230.000

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 head0.102
GPT teacher head0.427
Teacher spread0.325 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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