Identity as pedagogy: appreciative inquiry into the stories of our pedagogies
Bibliographic record
Abstract
Teacher identity is a dynamic nexus of experiences and narratives on which teachers draw to understand and situate themselves in teaching. Additionally, teacher identity becomes pedagogy as we teach who we are. This narrative inquiry used an Appreciative Inquiry (AI) framework to explore stories of teaching that have interwoven into the teacher identities of a group of teachers, who encompassed various roles within a single educational jurisdiction. In this study, the term teachers broadly included individuals who are part of an educational system including classroom teachers, principals, and system leaders. Each participant teacher drew upon their lived experiences of narratives from within a common educational system that shaped their identity and pedagogy. Through five AI phases, participants named and confronted common system narratives they had encountered in teaching. Furthermore, they reframed these narratives in ways that might potentially bring about system change. The findings from this study can support teachers, teacher educators and system leaders to examine biases and beliefs embedded in pedagogy and, through critical reflection, consider ways system change might emerge.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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".