Self‐study in teaching and teacher development: a call to action
Bibliographic record
Abstract
This article maps out key features of self‐study in teaching and teacher development, particularly in relation to social action. As teacher educator‐researchers, we have become increasingly interested in how self‐reflexivity in teaching and teacher development can illuminate social and educational challenges that have resonance beyond the self and can inspire context‐specific, practitioner‐led responses to those challenges. Drawing on creative and participatory approaches to engaging in self‐study, we highlight some of the ways in which the personal interconnects with the social and in so doing contributes to taking action. The examples that we use illustrate the use of personal narrative and video documentary, particularly in the context of our work with South African teachers, and point to the potential for ministries and faculties of education to support self‐study initiatives as an approach to social action and community development.
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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 | 0.199 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.030 | 0.227 |
| Scholarly communication | 0.040 | 0.061 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.034 | 0.045 |
| 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".