What's happening in coaching and mentoring? And what is the difference between them?
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a summary of the latest developments in the field of corporate coaching and mentoring. Design/methodology/approach Provides a viewpoint on the coaching and mentoring field drawing on research from Europe and the US. Findings Structured or supported coaching and mentoring within organisations is evolving rapidly and research is at last beginning to provide valuable insights into effective practices. Some strongly‐held assumptions are being challenged along the way. Greater definitional clarity, within specific contexts, contributes to efficacy. Increasing professionalisation with the coaching and mentoring sector is being helped by dialogue between the various bodies representing coaches and mentors and by the spread of supervision. Originality/value The article provides a succinct overview of the current position of the corporate coaching and mentoring arena and offers insights into how the field will develop in the future.
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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 | 0.035 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| 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".