Practical ethical leadership: Alberta-based research studies on instructional leadership
Bibliographic record
Abstract
This paper is a synthesis of an Alberta Teachers' Association (ATA) sponsored case study of five highly-effective elementary schools in Alberta. Researchers spent time in these schools asking teachers, principals, and support staff two questions: (a) what makes this school such a good place for teaching and learning; and (b) what does the administration do to help? Data were gathered through interviews; and comprehensive notes were made, organised, analysed, and synthesised into case studies for each of the five schools. While the original study asked and answered three questions: (1) what (what did we find); (2) so what (what do these findings mean); and, (3) now what (what should we do after we make sense of the findings), this paper focuses on analysing and sharing findings about question (3). Because this practical conceptual framework suggests principal best practices, the paper we offer here is not intended to report specific findings as much as to thoughtfully consider what our research findings suggest for the ethical and practical actions of principals. Our book Living Leadership for Learning: Case Studies of Five Alberta Elementary School Principals (Parsons and Beauchamp, 2011) outlines our findings in detail.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".