Bibliographic record
Abstract
In Ontario, Canada, principals’ work is guided by competencies outlined in the Ontario Leadership Framework which reflect the leadership skills and behaviours needed to successfully lead schools, improve student achievement and well-being. The problem is that this document largely ignores equity and anti-oppressive leadership behaviours. The Toronto District School Board (TDSB), created an addendum to this document, introducing the notion of equity as a leadership competency. Other documents of this nature have emerged in multiple systems in Ontario in response to the increasing need to provide guidance to principals about how to engage equity work. The challenge is the gap between the competencies on the page and the actions taken in schools. There is little guidance around how to make these competencies actionable. This paper engages a discourse analysis of the TDSB’s equity as a leadership competency document resulting in the creation of a model for equitable action. A focus on action-oriented language used in each competency revealed three overarching themes: development of self, building, connecting, and creating, and accountability. The model is intended to be used as a decision-making tool to help leaders ask the right questions and guide their thinking towards equitable action and the disruption of the status quo. Critical Race Theory and notions of critical hope inform this work to ensure that the model is well positioned to guide leaders towards actionable and transformative change.
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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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.064 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".