Reifying discrimination on the path to school leadership: Black female principals’ experiences of district hiring/promotion practices
Bibliographic record
Abstract
Using intersectionality as a guiding framework, this qualitative study focuses on the hiring/promotion experiences of 20 Black female principals and explores how their hiring/promotion practices reified and/or interrupted traditional discriminatory pathways to school leadership. We find that gendered racism operated across all facets of the principal recruitment and hiring processes in which these women participated. First, relationships and political connections with those already in power (e.g., predominately White men) seemed to be a key mechanism for entering the applicant pool and, later, accessing leadership opportunities. Opportunities were often explicitly racialized such that considerations for leadership positions were stated as being based on the participants being Black. Second, interview processes were frequently described as more performative than substantive with many of the women highlighting questions and comments that reinforced problematic tropes about Black women. Questions also abounded about whether interview panels were reflective of the community and/or if the questions were standardized to ensure fairness and transparency. Finally, district level hiring decisions were frequently disconnected from the interview process and lacked transparency with superintendents, in particular, who overrode or ignored prior steps in, or recommendation from, the school-based part of the process. In this way, findings suggest a hiring/promotion system desperately in need of revision starting with the most basic design features (e.g., standardized interview questions, transparent performance indicators, process accountability via decision-making) and including disrupting discrimination across all facets of the system.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".