Bibliographic record
Abstract
Purpose To describe the contextual supervision (CS) model, and to invite interested researchers to study its effects in a wider range of applications across a variety of management fields. Design/methodology/approach The developer of the CS model summarizes how he refined and studied the original situational leadership approach to assist supervisory personnel in education to mentor teacher‐interns as they developed their classroom instructional skills. Findings The 15 years of accumulated CS findings have consistently identified several strengths and one lingering limitation with the model. Key strengths are that CS is intuitively appealing and relatively easy to learn and that it helps participants clearly conceptualize the entire supervisory process. The limitation is that there appears to be a small, but persistent, number of supervisors who, although trained in CS, tend to exhibit a mismatch of style with supervisee developmental level. Practical implications There is enough research evidence to suggest that the CS model has potential to be adapted and studied by managerial personnel across a variety of other supervisory areas; and that it could enhance supervisors’ mentoring of protégés engaged in learning and/or improving the skills and knowledge specific to their particular fields. Originality/value The author invites collaborative inquiry across disciplines in order to have scholars and practitioners consider applying the CS model in their mentoring activities; and also to study and to disseminate the results in order to add to the research base on CS.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".