Bibliographic record
Abstract
The movement of AI into the coaching arena continues to be steady and confident, meeting only rare and timid resistance. The progress of this movement can be explained by decades of technological advances, the entrepreneurial attitude of AI developers, and the inherent peculiarities of the coaching business. The voices of caution are too quiet in ‘the noise of progress’. However, there are important reasons for coaching communities to be apprehensive about the ways this movement could change coaching as a service and what this means for all involved. In this paper, I address potential problematic issues with the AI revolution in the context of a multitude of conceptual holes in coaching as a profession. I argue that dehumanising coaching under the guise of ‘enhancement by AI’ undermines human intelligence, which is desperately needed while the discipline of organisational coaching remains in its early stages of development.
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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".