Hiring for Diversity: Changing the Face of Ontario's Teacher Workforce
Bibliographic record
Abstract
Changing the face of Ontario’s teachers is a topic that draws both ardent support and profound criticism by those closest to school governance. It surfaces questions about teacher quality, student achievement, inequity, diversity, politics, public trust, the past, the present, and the future. In the centre are teachers applying for jobs, and principals making decisions about whom to hire, and why. Influencing these decisions are a number of elements that range from the rational procedures that underlie human resources management to the intuitive, difficult to define factors borne of principals’ experiences and perspectives on teacher excellence. This study attempts to examine the processes at play as principals go about hiring teachers for the specific purpose of diversifying the teacher complement in their schools. Despite pressure from public policy since the 1980s, movement towards greater diversity in the teacher workforce has been slow, showing only modest change while the ethno-racial diversity of Ontario’s large urban centres approaches or surpasses fifty percent. Surveys from nearly a quarter of the principals in District A (a large, suburban school district in the Greater Toronto Area) and follow-up interviews with ten of their colleagues provide data that reflect how principals position diversity relative to other teacher qualities when hiring, their understanding of policy intimating the need for more teachers from diverse backgrounds, and the internal and external hurdles they face when hiring teachers. The data also show that these factors do not often include dimensions of teachers’ diverse identities. This finding remains surprisingly consistent among principals across variables such as experience, the number of interviews they conduct, and the student diversity of their respective schools. Data collected from personal interviews, however, show more nuanced differences between principals’ hiring stories and reflect a strong, unresolved tension between a belief in the benefits of teachers from diverse backgrounds and the hiring decisions that manifest this belief. Of note: the vast majority of participants in both the survey and interviews self-identify as White European. As such, the data are interpreted as a representative sample of principals in District A, but also as the prevailing identity of those charged with hiring teachers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".