Comparative and critical analysis of competency standards for school principals: Towards an inclusive and equity perspective in Québec
Bibliographic record
Abstract
This article presents the results of a comparative and critical study of the competency standards of Québec school administrators compared with seven other education systems within the Organisation for Economic Co-operation and Development (OECD). An inductive-type analysis has made it possible to identify the social categories targeted in the standards, the vision of school leaders as well as the competencies that are likely to help advance educational and societal goals of equity, inclusion, and social justice. Three contrasting perspectives emerge from this analysis. In Australia, California, and the United States, principals are explicitly encouraged to take action against structures and practices that undermine the educational success and social recognition of minority groups. In British Columbia and New Zealand, statements about social diversity focus more on the transformation of individual practices. Finally, in the standards of England, Texas and Québec, only a few generic statements referring to the differentiated needs of students and their success have been identified. They are instead characterized by a managerial approach oriented toward results that are measurable and cost controlled. In conclusion, a more in-depth analysis of the Québec standards opens the door to a new competency model and recommendations.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".