RETRACTED: The Possibilities and Impossibilities of Transformative Leadership: An Autoethnographic Study of Demographic Data Policy Enactment in Ontario
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Policy discourses of equity, diversity, and inclusion (EDI) have influenced Ontario’s K-12 education system for decades. Recently, EDI education policies have mandated that district school boards collect demographic data from students and staff. The purpose of this research is to examine the enactment of demographic data collection policies in one Ontario school district through an exploration of the policy enactment activities of the research leader who was responsible for demographic data collection projects. Drawing on theories of policy enactment and transformative leadership, this research interrogates how provincially mandated demographic data collection policies are translated in local contexts and shape policy responses and practices. This research employs an autoethnographic methodology to illuminate the diverse policy positions and policy work of the research leader. The narrative of policy enactment is one that includes complexity and contradiction in terms of the enactment and outcomes of demographic data collection policy. Ultimately, conflicting organizational cultures, hierarchies, and limited material resources all served to constrain the enactment of demographic data collection projects in ways that would support transformative, anti-racist outcomes.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".