How can Antifragility Help Theorize Coaching in a Volatile and Unpredictable World?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it