Leading by Serving: Enhancing Collective Teacher Efficacy
Bibliographic record
Abstract
The COVID-19 pandemic and neoliberal ideologies in Alberta have presented significant challenges for teachers, especially with the implementation of the new curriculum rollout beginning in 2022. Work intensification has emerged as a considerable problem, adversely affecting teachers' well-being and performance. This Dissertation-in-Practice examines this problem within the context of a public charter school for gifted students. It addresses this issue by focusing on collective teacher efficacy, guided by Bandura's social cognitive theory. A multifaceted leadership approach is adopted to tackle this challenge, guided by servant leadership and supported by instructional leadership, generative leadership, and culturally responsive school leadership. The central focus revolves around planning and implementing solutions to effectively lead change, explicitly addressing the identified problem of work intensification and the lack of collective teacher efficacy. To achieve this objective, the preferred solution is fostering a collaborative culture promoting reflective practice within professional learning. A detailed change implementation plan is developed, guided by the change path model. This change is disseminated through a knowledge mobilization plan, ensuring transparency and understanding among community partners. Finally, the implemented change will be evaluated using the Plan-Do-Study-Act cycle, providing valuable insights for continuous improvement. The overarching goal is to establish a supportive and collaborative school culture that mitigates work intensification and fosters collective teacher efficacy, thereby positively contributing to teachers' overall well-being and performance within gifted education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".