Self-Study Research in a New School of Education: Moving between vulnerability and community
Bibliographic record
Abstract
This paper explores the dual and seemingly contradictory potential of self-study research to illuminate our fears, anxieties, tensions and uncertainties as teacher educators, whilst acting as a catalyst for community building. This self-study research was conducted during the founding year of a new school of education, drawing data from surveys and interviews with faculty about their own self-study research and participation in one another's studies. Through these collective self-studies, faculty members constructed and negotiated their identities as teacher educators and as a school of education. As researchers and researched participants, the faculty of the new school of education moved during that first year between vulnerability and community, a process illuminated by their self-study research.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| 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".