Invoking teacher agency and professional identity through <i>Currere</i> : insights from within Türkiye’s centralized education system
Bibliographic record
Abstract
This study examines how teachers’ professional agency and identity development emerge through their sensemaking of critical incidents across the temporal dimensions of currere within Türkiye’s centralized education system. Using the phenomenological methodology, in-depth interviews were conducted with 16 teachers selected through maximum variation sampling from public schools in Ankara. Data were analysed through an integrated approach combining critical incident analysis with thematic analysis within currere’s framework. The findings suggest how teachers construct and exercise their agency through critical incidents across four temporal and agentic dimensions: regressive, progressive, analytic, and synthetic. The study potentially extends currere methodology by demonstrating its effectiveness in understanding teacher agency development within centralized systems, particularly highlighting how critical incidents serve as catalysts for professional growth across temporal dimensions. Results illustrate how teachers successfully integrate personal agency with institutional requirements through temporal reflection on critical incidents, developing professional identities characterized by autobiographically-mediated institutional agency. This research contributes to understanding how structured temporal reflection enhances teacher agency and professional identity, offering practical recommendations for teacher education and development programmes in centralized systems.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".