Talent Identification in Youth Ice Hockey: Exploring “Intangible” Player Characteristics
Bibliographic record
Abstract
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 ofwhyintangibles were important,whatintangibles were valued, andhowscouts 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".