Bibliographic record
Abstract
Introduction The Contextual Side of Professional Boxing Re-visited Preparing Professional Hockey Players for the Playoffs A Case for a New Sport Psychology: Applied Psychophysiology & fMRI Neuroscience The Psychology of Being an Olympic Favorite Psychological Preparation of Athletes for the Olympic Context: Team Culture & Team-Building From One Olympics to the Next: A Four-Year Psychological Preparation Program Introduction to Cultural Sport Psychology Revisited Revisiting Diversity & Politics in Sport Psychology Through Cultural Studies: Where Are We Five Years Later? Sport Psychology as Cultural Praxis Theoretical Approaches to Cultural Sport Psychology Through the Funhouse Mirror: Understanding Access & (Un)Expected Selves through Confessional Tales Using Psychological Skills Training from Sport Psychology to Enhance the Life Satisfaction of Adolescent Mexican Orphans Sport Psychology Consulting with Latin American Athletes A Model for Supervision of Applied Sport Psychology Consultations in Division I College Sports Ethical Decision-Making in Sport Psychology: Issues & Implications for Professional Practice Sport Psychology Consulting with Canadian Olympic Athletes & Coaches: Values & Ethical Considerations A profession of violence or a high contact sport? Ethical issues working in professional boxing Commentary Chapter Index.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.099 | 0.028 |
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".