Scholarship-of-Leading-Mini-Cases November 2019
Bibliographic record
Abstract
<b>The Scholarship of Leading</b>Mini-Cases of Educational Leadership in Action<br><br><br>To cite:Rolheiser, C., Carbone, A., Macnab, E., & Ye, J. (Eds.). (2019). <em>The scholarship of leading: </em><em>Mini-cases of educational leadership in action</em>. Toronto, ON: Centre for Teaching Support & Innovation, University of Toronto. https://teaching.utoronto.ca/sotl/<br>scholarship-of-leading/ <br>Also available at http://aautn.org/resources/<br>Authors:<br>Carol Rolheiser<br>Professor, Department of Curriculum, Teaching & Learning, Ontario Institute for Studies in Education (OISE) & Director, Centre for Teaching Support & Innovation<br>University of Toronto<br>CanadaAngela Carbone<br>Professor, Department of Computer Science and Software Engineering, & Associate Dean Learning Innovation, Faculty of Science, Engineering and Technology<br>Swinburne University of Technology<br>AustraliaErin Macnab<br>Coordinator, Programs & Strategic Initiatives, Centre for Teaching Support & Innovation<br>University of Toronto<br>CanadaJing Ye, Ph.D.<br>National Awards Project Coordinator, Faculty of Science, Engineering and Technology<br>Swinburne University of Technology<br>Australia
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.381 | 0.249 |
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".