A CANADIAN TEACHER'S PERCEIVED AUTONOMY AND SELF-CONFIDENCE IN THE MIDST OF AN EDUCATIONAL REFORM
Bibliographic record
Abstract
Autonomy is essential for teachers’ commitment, satisfaction and retention. However, teacher autonomy is consistently being reduced in the current era of reforms and the sharing of global policies. In this narrative case study, the account of one Canadian teacher (Nora) was examined in relation to how an educational reform influenced her work life in terms of her perceived autonomy and self-confidence. The relation between teacher autonomy and self-confidence was addressed, and the ramifications prompted by the fluctuations in both constructs. The findings suggest that the relationship between Nora’s perceptions of teacher’s professional autonomy and her self-confidence is complex. Nora’s self-confidence was strongly related to her perceived autonomy and the reduction of autonomy triggered significant downward spiralling, leading to isolation. Some discussion on the possible ways to help teachers break this downward spiralling is provided. Finally, this research broadens the collection of stories by teachers on challenges introduced by educational reform and gives voice to teachers suffering in isolation. Key words: educational reform, narrative inquiry, perceived self-confidence, teacher autonomy, teacher isolation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".