Preparing School Leaders for Teacher Growth, Supervision, and Evaluation
Bibliographic record
Abstract
This research explores the ways in which sustained generative leadership development impacts school leaders’ actualization of a provincial Teacher Growth Supervision, and Evaluation (TGSE) Policy (Government of Alberta, 1998). Brandon et al (2018) reported that teachers’ perceptions of the benefits of ongoing supervision markedly diverged from those of school and jurisdictional leaders; and that there was a conflation of understanding among teachers, school leaders, and jurisdictional leaders about the practices that constitute growth, supervision, and evaluation. This present research uses the same instrument implemented in the Brandon et al study to seeks to answer the question To what extent and in what ways do teachers, school leaders, and system leaders perceive that ongoing supervision provides teachers with the support necessary to be successful? To measure these perceptions, an online survey will be administered to teachers, school leaders, and system leaders in a mid-size jurisdiction that has implemented a generative leadership development program over two years. Survey results will be compared with large-scale findings across a randomly sampled population (Brandon, et al, 2018). Findings from this study will formulate recommendations relevant to policy development, leadership preparation programming, and the actualization of teacher growth, supervision, and evaluation strategies.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".