A Narrative Inquiry of Teacher Perceptions of Autonomy During Emergency Remote Teaching
Bibliographic record
Abstract
Research indicates that professional autonomy plays an important role in determining overall levels of teacher job satisfaction (Johnson & Spector, 2007), leads to increased teacher retention rates (LaCoe, 2006), and positively impacts student achievement (Preedy, Bennett, & Wise, 2012). A study conducted by Hyslop-Margison & Sears (2001) established a link between educational quality and teacher autonomy, with researchers indicating that “the quality of education is undermined when teachers are held accountable to an external authority rather than to themselves, their colleagues, and their professional associations” (p. 1). Similarly, Preedy, Bennett, & Wise (2012) established the presence of positive impacts on student achievement when educational goals were established by teachers themselves rather than by external agents in the education process. With evidence to suggest that professional autonomy is mutually beneficial for both students and teachers, classroom-based educators continue to report dissatisfaction with overall levels of autonomy, while teachers’ perceptions of professional autonomy continues to decline (Walker, 2016). Set in the context of emergency remote teaching during the Covid-19 pandemic, my work will uncover factors identified as negating or promoting teacher autonomy, while identifying how some educators appear more successful in attaining autonomy over others under similar environmental and social conditions. A qualitative narrative inquiry methodology is undertaken to identify and explore the understandings and experiences of educators as they navigate their need for autonomy in Alberta’s current educational climate. Semi-structured interviews, observations, and field notes used during the data collection stage, are analyzed to derive answers to questions about participants’ experiences with and perspectives on teacher autonomy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".