Embracing the Race: How Black Women Principals Construct Their Professional Identities in Ontario Schools
Bibliographic record
Abstract
Although developments in provincial and school-level policies have increased the number of Black leaders in Canadian K-12 schools, research on Black women leaders continues to be disproportionality underrepresented in education leadership and administration (ELA) research. Of the studies conducted on Black principals, the intersection of their race-and-gendered identity markers are not adequately addressed nor presented in most literature (Armstrong & Mitchell, 2017; Lomotey, 2019; Mponguse, 2010; Nickens & Washington, 2017), particularly in spaces where the study of principals’ professional identities is linked to achieving school reform initiatives. While the extant literature on school improvement acknowledges principals as key sources of knowledge, there remains a space in ELA literature for a deeper interrogation of Black women principals’ professional identities in a Canadian context. In response to this gap in the literature, the present qualitative study uses a narrative life history (life history) approach to examine how seven Black women principals construct their professional identities in Ontario school districts. Focusing on the historical, political, and sociocultural tensions that encompass the race-and-gendered identity of the Black woman, this study draws on the tenets of intersectionality as a conceptual framework for situating the narratives shared by participants.\nFindings from semi-structured interviews reveal that Black women obtain leadership positions based on contingent situations and context-related circumstances, that is, through shoulder-tapping or employment equity initiatives—where being at the right place at the right time affords them entrance into leadership. When finally in these roles, Black women must then construct their professional identities in racially contentious environments characterized by a lack of organizational supports, absence of mentorship, and limited career advancement opportunities. All while simultaneously being held to higher standards of practice than their counterparts. This dissertation offers novel strategies for re-examining professional standards outlined in the Ontario Leadership Framework (OLF; The Institute for Education Leadership, 2013), the deployment of school board mentorship programs, and principal recruitment processes. Given that school improvement initiatives identify principals as key agents for change, this study provides significant insights and contributions for leadership theorization, school leader preparation program development, and practitioners’ understanding of principal practices in Ontario’s K-12 public schools.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.023 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".