Making Sense of Coach Development Worldwide During the COVID-19 Pandemic
Bibliographic record
Abstract
The commentary brings together the perspectives of a group of coach developers from across the globe who form a community of practice (CoP) from their involvement as “Cohort 5” in the International Council for Coaching Excellence and Nippon Sport Science University Coach Developer Academy. The CoP includes people from three types of organizations: university professors of sport coaching programs, national sport federations, and national multisport organizations’ directors of coach education. While this CoP existed prior to the pandemic, the forced isolation has created a new structure and purpose to the CoP: The authors are all making meaning of the landscape of coach development within which they work by understanding the perspectives of others who work in their domain from across the world and the similar realities that they face in North America, Europe, the United Kingdom, and New Zealand. The authors outline the key themes that emerged from their weekly CoP video conference meetings to shed light on how this pandemic has changed the way they think about coach development.
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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.036 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".