The power of collective narratives to inform public policy: reconceptualizing a Principal’s Qualification Program
Bibliographic record
Abstract
A narrative approach to policy development was utilized to collaboratively reconceptualize a provincial Principal’s Qualification Program. The stories, perspectives and lived experi-ences of teachers, parents, students and the public were included as essential voices and information sources within policy development conversations. These collective narratives of experience revealed the forms of knowledge, skills, dispositions and ethical commit-ments necessary for effective principals today and in the future. They also illustrated the transformative nature of narrative to enlighten, deepen understanding and alter perspec-tives. The policy development processes used in this publicly shared educational initiative are a model of democratic dialogue. The inclusive and dialogic methods employed to col-lectively reconceptualize a principal formation programme illustrate an innovative frame-work for developing policies governing the public good. Given the change in demographics in Ontario, future administrators MUST have a solid understanding of the dynamics of power and privilege. A module in the Principal’s Program should be created that examines this dynamic, along with the concepts of racism, homophobia, Islam phobia, ableism, classism and discrimination. There is no doubt that minority groups are
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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 | 0.079 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| 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".