The Coaching Black Box: Risk Mitigation during Change Management
Bibliographic record
Abstract
A case study of strategic renewal in the Chinese education market, this paper explores a non-directive coaching model and its impact on risk mitigation, knowledge exchange and innovation in strategic renewal through the application of multi-tiered coaching and manager coaches. Through an ethnographic action research methodology, we ask “Can coaching mitigate organisational risk and increase the likelihood of positive outcomes in change management?” and “Can managers, acting as internal coaches, increase knowledge socialisation and mitigate risk in the change management process?” The paper finds that there is no inherent failure rate in the change management process and that a strategic management approach can mitigate risk liberating managers and organisations to seek to create the collaborative environments that support organisational learning and strategic renewal, thus moving beyond a narrative of failure to one of strategic empowerment and a strategic management approach to risk mitigation. We conclude that a data-driven approach to organisational learning and Professional Learning Communities helps teams to ask the right questions and to mitigate risk through better aligning the organisation to its strategic reality, exploiting organisational learning to achieve competitive advantage and ensuring that systems and processes continue to match the emerging strategic reality.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".