Epistemological criticism of coaching for a transition to humanistic transformation
Bibliographic record
Abstract
What is the purpose of coaching? The answer to this question only becomes clear when we understand, or agree on, its philosophical foundation, its concept of the human being. Yet the attraction of coaching for many is its openness, and its resistance to definition. This very openness has its dangers, limiting the discipline to performance in sports and business, allowing for bad faith actors, and impeding intellectual growth. This paper will provide an overview of the major influences in the early development of coaching, from implicit cultural factors to applicable innovations in psychological modalities. This critical overview becomes the springboard from which we can dive into an “ethical shift” in coaching, which takes as its starting point the fulfilment of the human being’s authentic potential, especially as advocated in the humanistic philosophy of Erich Fromm. We then show how this shift can be integrated into existing forms of coaching to increase its effectiveness. In the end, we will see how performance is enhanced by the individual’s existential wellbeing, and how the individual’s wellbeing is enhanced when performance has meaning for them and harmonizes with their values.
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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.132 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".