Subverting Whiteness and Amplifying Anti-Racisms: Mid-Level District Leadership for Racial Justice
Bibliographic record
Abstract
This counternarrative study positions two distinct bodies of literature in conversation: mid-level district leadership in the literature on educational change and anti-racist approaches to leadership framed through Critical Race Theory and Critical Whiteness Studies. Interviews with twelve, mid-level district leaders committed to anti-racism in Ontario, Canada, reveal fundamental differences in leaders’ knowledges and capacities compared to those identified in the literature on educational change and promoted in the corresponding leadership frameworks in Ontario. In centering power, racialization, and whiteness as a logic of oppression, anti-racist approaches to leadership fundamentally reconstitute conceptions and enactments of leadership. Findings speak to the importance of knowledge(s) about race and racialization, racism and intersecting oppressions, and how whiteness subverts anti-racist efforts. Findings also speak to developing capacities such as: visioning that both owns historical injustices and imagines future possibilities; organizing and collectivizing as a means of power sharing and decentering the individual leader; facilitating difficult learning in the face of racist resistance and multiple frameworks; securing accountability for rights by building informal accountability structures while advocating for formal ones; aligning resources and creating structures in support of students from historically oppressed communities; and, sustaining the self in the face of the impending harm in doing this work. With a focus on whiteness, this study invites scholars and practitioners to turn the gaze upward and consider what might need to be undone and unlearned from multiple and intersecting systems of oppression, what the authors refer to as unleading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".