How can Antifragility Help Theorize Coaching in a Volatile and Unpredictable World?
Bibliographic record
Abstract
This conceptual article explores how antifragility might support a fuller theorization of coaching in the context of a world characterized by increasing levels of complexity, volatility and unpredictability. Antifragility, a term which describes how certain systems become stronger when exposed to volatility, has been embraced by and applied within a range of disciplines and industries but is yet to receive any substantive attention in the coaching literature. This article introduces the concept of antifragility and explores how antifragility might support a process of greater critical reflexivity in how the purpose of coaching is conceptualized and the types of coaching conversations it might facilitate. It is proposed that antifragility offers a valuable lens for re-examining and redefining some of the under-theorized norms of coaching including the still largely unchallenged assumptions concerning the benefits of performance enhancement, growth and efficiency. In doing so, this article seeks to add to the growing number of scholars who are calling for coaching to reposition itself as a vehicle for social change rather than a method of individual and organizational optimization.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".