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Record W3108852993 · doi:10.3389/fpsyg.2020.607710

Talent Research in Sport 1990–2018: A Scoping Review

2020· review· en· W3108852993 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Psychology · 2020
Typereview
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsOntario Tech UniversityUniversity of OttawaYork University
Fundersnot available
KeywordsPsychologyAthletesInclusion (mineral)Identification (biology)Applied psychologyPerceptionMedical educationSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Several recent systematic and targeted reviews have highlighted limitations in our understanding of talent in sport. However, a comprehensive profile of where the scientific research has focused would help identify gaps in current knowledge. Our goal in this scoping review was (a) to better understand what others have done in the field of research (e.g., what groups have been examined using what research designs and in what areas), (b) to summarize the constituent areas of research in a meaningful way, (c) to help identify gaps in the research, and (d) to encourage future research to address these gaps. Peer-reviewed articles written in English that met several inclusion criteria were analyzed. A total of 1,899 articles were identified, and the descriptive findings revealed a relatively narrow focus of research on talent in sport. Specifically, the majority of examined articles focused on (a) males only, (b) the sport of soccer, (c) perceptual cognitive variables, (d) developing athletes, (e) adult samples, and (f) cross-sectional designs. For better or worse, the concept of talent remains a central element of how coaches, practitioners, and scientists think about athlete development. Findings from this scoping review highlight the continued need to explore issues related to talent identification, selection, and development in more diverse samples (e.g., female athletes and younger ages) and contexts (e.g., from Africa, Asia, and South America). There is also a clear necessity to focus on under-researched areas using alternative methodologies.

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.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.730
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.004

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 head0.200
GPT teacher head0.526
Teacher spread0.326 · 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