Contextualizing Teacher Professionalism: Findings from a Cross-Case Analysis of Union Active Teachers
Bibliographic record
Abstract
This paper draws on data collected as part of a study of the discourses of teacher professionalism amongst union active teachers in the Canadian provinces of Alberta and Ontario. Interviews revealed a triad of influences on the professionalism discourses of participants: engagement in teacher associations, the larger policy environment, and teacher agency. The manner in which this triad played out in each case, however, was unique to the particular political and organizational contexts framing the spaces in which such discourses were created. Using cross-case analysis, this paper specifically highlights the complex and contextualized nature of teachers’ conceptions of professionalism, paying particular attention to the nuanced enabling and limiting conditions identified between the cases.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".