Towards a more symmetrical approach to the zone of proximal development in teacher education
Bibliographic record
Abstract
ABSTRACT This article focuses on relations between a new teacher and a teacher educator. It draws on the zone of proximal development (ZPD) studies, and the data is analyzed through conversation analysis. Ordinarily, the ZPD is used to theorize the learning that occurs in such a relation in asymmetrical terms. Our case study shows, however, that learning occurs for both participants in the relation, and that the very question of who becomes “the more competent peer” arises from the relation that constitutes a ZPD. Therefore, there are dialectical inversions, whereby the actual roles of teacher and learner no longer coincide with the institutionally designated positions of particular individuals. This then requires an approach to the ZPD that allows for the changes in the relation such that who teaches and who learns is itself the result of the social relation.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".