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Record W2924080290 · doi:10.1123/tsp.2018-0155

Talent Identification in Youth Ice Hockey: Exploring “Intangible” Player Characteristics

2019· article· en· W2924080290 on OpenAlexaff
Ryan W. Guenter, John G.H. Dunn, Nicholas L. Holt

Bibliographic record

VenueThe Sport Psychologist · 2019
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBoy ScoutsIce hockeyLeaguePsychologyThematic analysisPassionIdentification (biology)Social psychologyPublic relationsAdvertisingApplied psychologyQualitative researchSociologySocial sciencePolitical scienceBusiness

Abstract

fetched live from OpenAlex

The purpose of this study was to examine “intangible” characteristics that scouts consider when evaluating draft-eligible prospects for the Western Hockey League. Sixteen scouts participated in semistructured interviews that were subjected to an inductive thematic analysis and then organized around predetermined categories of why intangibles were important, what intangibles were valued, and how scouts evaluated these intangibles. Intangibles helped scouts establish players’ fit with the organizational culture of teams and influenced scouts’ draft-list ranking of players. The key intangibles scouts sought were labeled compete, passion, character, and leadership/team player. Scouts noted red flags (i.e., selfish on-ice behaviors, bad body language, and poor parental behavior) that led them to question players’ suitability for their respective organizations. Finally, scouts used an investigative process to identify and evaluate these intangibles through direct observation; interviews with players, coaches, and trainers; and assessments of players’ social media activities. Implications for sport psychology consultants are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.008

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 head0.079
GPT teacher head0.328
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2019
Admission routes1
Has abstractyes

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