Mentoring, not Monitoring: Mediating a Whole-School Model in Supervising Preservice Teachers
Bibliographic record
Abstract
This article offers a case study of a whole-school model for supervising preservice teachers that is related to the principles of professional development schools (Holmes Group, 1990) and Zeichner's (1992) notions of rethinking student teachers' practicum experience. The article draws on constructivist notions of learning to teach; in particular, reference is made to Vygotsky's (1978) sense of mediating a stimulus where in a social-cultural context a person's knowledge is created, examined, and transformed rather than simply absorbed and transmitted. The case study highlights the particular details of this whole-school model and connects these to five main empirical descriptors that were generated from data triangulated from preservice teachers' journals, evaluation forms, group meetings, and correspondence from cooperating teachers. The descriptors show how the whole-school model evolved and how the role of university facilitator shifted from monitoring to mentoring in the teacher preparation process. This change in relationship disrupted the isolating clinical model of supervision where the university facilitator and cooperating teacher, in an uneasy relationship, are perceived by preservice teachers as having power over them: "telling" the preservice teacher with little perceived opportunity for negotiation.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".