Contributing to the Draft Process in Professional Sport Organizations in the United States and Canada: Perspectives and Guidelines for Sport Psychology Practitioners
Bibliographic record
Abstract
Sport psychology practitioners may be asked by general managers and coaches of professional sport organizations to contribute to the draft process by providing psychological information about athletes they want to select for their respective teams and organizations. Although it seems that this request for psychological information is increasing in professional sport organizations in the United States and Canada, little guidance has been forthcoming about how sport psychology practitioners can contribute to the draft process. This article provides perspectives on the nature and scope of the draft process based on guidelines for psychological assessment and evaluation, consultation theory and research in sport psychology, and the professional experiences of the authors. It also offers guidelines for how sport psychology practitioners can engage in the draft process in professional sport organizations in the United States and Canada.
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 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 | 0.393 | 0.647 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.071 | 0.042 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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.
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".