Educational administrators' conceptions of whiteness, anti‐racism and social justice
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the intersections of whiteness, anti‐racism and social justice in educational administration. It is an attempt to understand how white administrators who work in racially minoritized school communities reconcile the moral challenges of articulations of racial equity with the hierarchical institutions of schooling. Design/methodology/approach This qualitative study asks ten white administrators how they understand themselves as raced, the ways they see race operating at individual and institutional levels in schools and districts, and factors that facilitate and/or hinder social justice work as it pertains to race. Findings The data indicates that whiteness is a difficult subject for white administrators, even those who agreed to be interviewed about whiteness, racism, equity and social justice. As agents of the school districts where they are employed, the administrators generally view these issues from an organizational perspective that does not challenge hegemonic structures. They typically understand social justice from non‐critical perspectives, see whiteness at the level of the individual, racism as unacceptable individual acts, and multiculturalism as preferable to anti‐racism. Research limitations/implications The findings cannot be generalized; however, they show that academic education and certification programs need to be revised in order to prepare administrators to deal with issues of locatedness and difference. Originality/value The study is set in a Canadian context where, in spite of overwhelming evidence that visible minority students are marginalized in and by school policies and practices, racism is often overtly and emphatically conceptualized as a phenomenon that happens in other times and places.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.045 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".