Leadership team coaching in practice : developing high-performing teams
Bibliographic record
Abstract
Chapter - 01: Introduction: High-performing teams - the latest research and development - Peter Hawkins Chapter - 02: What are leadership team coaching and systemic team coaching? - Peter Hawkins Chapter - 03: Learning from case studies and an overview of published case studies - Peter Hawkins, Catherine Carr and Jacqueline Peters Chapter - 04: Coaching the commissioning and clarifying: A case study of a professional services leadership team - Hilary Lines Chapter - 05: Coaching the co-creating within the team: Two case studies from Canada - Catherine Carr and Jacqueline Peters Chapter - 06: Coaching the connecting between a new CEO, her leadership team and the wider middle management in a UK National Health Service organization - Jacqui Scholes-Rhodes and Angela McNab Chapter - 07: Coaching the team working with its core learning - Sue Coyne and Judith Nicol Chapter - 08: Team coaching as part of organizational transformation: A case study of Finnair - David Jarrett Chapter - 09: Team coaching for organizational learning and innovation: A case study of an Australian pharmaceutical subsidiary - Padraig O'Sullivan and Carole Field Chapter - 10: Inter-team coaching: From team coaching to organizational transformation at Yeovil Hospital Foundation Trust - Peter Hawkins and Gavin Boyle Chapter - 11: Evaluation and assessment of teams and team coaching - Peter Hawkins Chapter - 12: Coaching the board: How coaching boards is different from coaching executive teams, with case examples from the private, public and voluntary sectors - Peter Hawkins and Alison Hogan Chapter - 13: Embodied approaches to team coaching - Peter Hawkins and David Presswell Chapter - 14: Developing the personal core capacities for systemic team coaching - Peter Hawkins Chapter - 15: Training systemic team coaches - Peter Hawkins and John Leary-Joyce Chapter - 16: Team coaching - where next? - Peter Hawkins
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".