Bibliographic record
Abstract
Armed with the knowledge about learning and learning styles gained in Part III, we can take another look at the practice of peer consultation. Where exactly does peer consultation fit in within Kolb's learning styles? It has been suggested (e.g. in McGill and Beaty, 1992, or in Weinstein, 1995) that action learning stimulates the whole learning circle and stimulates every one of Kolb's learning styles equally. Our own research (see Appendix E for a summary) has shown a rather different situation, where action learning seems to stimulate one learning style — divergence — more than the others.1 Peer consultation consists primarily of reflection and an exchange of ideas on the basis of (previous) experiences in work situations, and this means that peer consultation is set primarily within the divergent learning style.KeywordsAction LearningConsultation GroupLearning StyleLearn NetworkAction GuideThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".