Teacher agency in the Canadian context: linking the<i>how</i>and the<i>what</i>
Bibliographic record
Abstract
Even though there is no common conceptual basis guiding teacher education in Canada, over the past two decades teacher educators both in Canada and around the world have called for teacher candidates to become agents of change. While researchers across Canada strive to demonstrate how to prepare pre- and in-service teachers to be agents of change, few scholars have examined in detail what teacher agency might mean in the Canadian context. This paper reviews the conceptualisation of agency from five theoretical perspectives (psychology, sociology, critical theory, historical studies, and post-structuralism) and examines how empirical studies in the Canadian contexts align with these perspectives. This paper makes explicit the connections between the how and the what of agency, and as such informs current approaches to preparing pre- and in-service teachers and their potential role as agents of change, and maps out how the notion of agency is taken up in a particular jurisdiction.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.041 | 0.025 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".