The Efficacy of the <i>Adaptive Mentorship</i><sup>©</sup> Model
Bibliographic record
Abstract
In this article the authors describe the Adaptive Mentorship? (AM) model that they designed, applied, and refined during the past two decades. They developed AM to be used within a variety of management, mentorship, coaching, supervisory, or training programs. After employing and researching it within educational settings, they received a federal grant to disseminate the model to a wider audience across the professional and occupational landscape and to investigate its effects. The researchers summarize the results of that experience, including their recent analysis of the judgments of several panels of experts regarding the efficacy of AM model. The authors present these findings for the consideration of practitioners, scholars, and researchers in any field interested in improving the mentorship offered in their own
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.012 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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; both teacher heads agree on what is shown here.
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".