Coaching Graduate Education: from Wild West to Established Territory
Bibliographic record
Abstract
The transdisciplinary field of coaching is growing rapidly and becoming better integrated into the development of human capital as a way to help individuals, groups, and organizations maximize performance. Today there are more than 40,000 people worldwide who identify themselves as professional coaches. In this fast-paced growth, coaching education programs proliferate, with a lack of consistency. In this symposium, we will discuss the challenges involved by the current evolution of coaching moving from its current “wild-west” state to a more “established territory”. We will thus share some current trends and issues facing the expansion of coaching and the move from merely training coaches to educating and developing coaching. We will explore what, if any, role higher educational institution can and should play and by what standards this work should be assessed. Topics for presentation and discussion include: ‘Taming the wild: The state of affairs in coach training and education’; ‘Exploring the Territory: Challenges facing the preparation of coaches’; ‘Mapping the Frontier: Coaching Competencies and Coach Education’; ‘Establishing the Territory: Program Standards’; ‘Forming a Nation: Coaching as an Emerging Area of Professional Practice and Academic Specialty’.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".