Predictive Processing: A Common Mechanism for Learning in Coaching Practice
Bibliographic record
Abstract
Coaching is a well-established practice. Yet given the many different approaches bearing the label coaching, all claiming to be at least as effective as the others, scholars and practitioners are left struggling to come up with a common conceptual framework. Here I propose that coaching approaches are united by a single learning mechanism to which we are all subject. Predictive Processing (PP) is an emerging theory of brain functioning which explains how humans learn by making and correcting ‘prediction errors.’ Since all coaching involves learning, working with this mechanism, whether explicitly or implicitly, is a key element in how coaching helps clients to achieve their aims. By expanding the common ground for dialogue between followers of different traditions, I hope it can contribute to the development of a more coherent theoretical foundation for coaching. By explaining the principles of PP and how they are reflected in models of learning and coaching practice, I also hope to show how they can help refine and deepen practitioners’ understanding of how coaching works.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".