Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the growing popularity of coaching; a concept whose influence increasingly spans academic disciplines and institutional fields. Design/methodology/approach The paper makes sense of coaching by using actor network theory, an approach that seeks to understand how a phenomenon becomes macro social. By examining a wide array of historical documents it traces the characteristics that underlie the transformation of the coach from a technological object to a management concept. In doing so it outlines the fundamental characteristics of coaching. Findings Specifically coaching involves a post technological nature where performances often occur in extreme conditions that involve the reciprocal interdependence of bodies (teams). These performances may also be viewed as involving impurity, as amateurs who participated purely for the love of the game have usually paid coaches for their services. Originality/value While there is no denying the influence of coaching, little attention has been given to the history of this concept. This article provides an example of how the past frequently remains present and offers explanation for the popularity of coaching. In doing so it outlines a potential framework for consistently discussing the concept across organizational forms.
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.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".