Research in Teacher Leadership in Canada: Transformative and Contextualized Agency
Bibliographic record
Abstract
This volume is the product of the collaboration of invited participants and editors at the Twelfth Working Conference of the Canadian Association for Teacher Education that was held at Brandon University from October 12-14, 2023. The focus of the conference was to bring together Canadian scholars engaged in teacher leadership, to engage in professional dialogue, to build a learning community, and to publish quality research. Teacher leaders operate “within and beyond the classroom” (Katzenmeyer & Moller, 2009, p. 5) to provide transformative leadership that fosters change in educational contexts. Localized knowledge, professional competence, and strong lateral relationships equip teacher leaders with the skills, confidence, and agency, to develop equitable learning networks, build capacity in others, and strengthen educational outcomes. The book is organized into four sections: A) Researching Teacher Leadership within K–12 Classrooms and Schools; B) Researching Teacher Leadership within K–12 School Systems; C) Researching Teacher Leadership beyond K–12 School Systems; and D) Researching Teacher Leadership within K–12 School Systems, and E) Researching Post-Secondary Teacher Leadership beyond Teacher Education Programs. This 26-chapter volume offers timely, high quality, context-specific research that makes a distinctively Canadian contribution to scholarship in the field of teacher leadership.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".