How and Why University Coaches Define, Identify, and Recruit ‘Intangibles’
Bibliographic record
Abstract
The purpose of this study was to explore the importance of psychosocial development in competitive university sport. While research in positive youth development (PYD) has increased in recent years, many perspectives have not yet been studied. The mission of PYD is to develop intangible qualities such as learning life skills, developing character, etc(Danish, Forneris, Hodge, & Heke, 2004; Gould & Carson, 2008). There is an implied and some times even an explicit conflict of interests between competitive and developmental sports (Shields & Bredemeier, 2009). In-depth interviews were conducted with 10 Canadian university coaches who were highly trained, experienced, and successful in their respective sports. Results from this study support three conclusions. (a) Coaches described essential intangible attributes such as social character, trustworthiness, maturity, challenging one’s self, being a positive person, toughness/resiliency, motivation, work ethic, and various sport psychology skills. (b) Competitive coaches highly value athletes with life skills, character, and other intangible assets. (c) Coaches have specific strategies for assessing and identifying players with stronger intangibles, as well as for filtering out recruits who severely lack these positive qualities.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".