Who’s supervising the supervisor? A case study of meta-supervision
Bibliographic record
Abstract
Global Practices and Training in Applied Sport, Exercise, and Performance Psychology offers case analysis as a vehicle to address issues and experiences in the application of sport, exercise, and performance psychology (SEPP) and the supervision/training of individuals to become professionals in the field. A follow-up to Becoming a Sport, Exercise, and Performance Psychology Professional (2014), this book features a discussion of real-world case examples which highlight various aspects of professional practice as well as supervision and training. Professionals from around the world, including the United States and Canada, Europe, Asia, Africa, and Australia share diverse experiences, providing a uniquely in-depth, global perspective. The case studies contained in the book were selected to provide insight into specific elements of applied practice and supervision/training through a global lens as well as demonstrate the value of incorporating case analysis and reflection into one’s training and continued professional development. Case analysis is an essential part of learning and instruction. Beyond educating the reader about theories and research on related topics in the field, case analysis allows for more complex levels of learning, including analysis, synthesis, and evaluation of diverse scenarios. In Part I of this book, the cases focus on applied SEPP practice; Part II is comprised of cases that focus on training and supervision. This book is essential reading for graduate students and neophyte professionals in the field for whom it is critical to learn how to effectively apply knowledge to real-world sport, exercise, and performance psychology scenarios. In addition, the book is a useful resource for seasoned and expert practitioners and supervisors who can use case analysis as a means of continuing their professional development.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".