Bias-Free or Biased Hiring? Racialized Teachers' Perspectives on Educational Hiring Practices in Ontario
Bibliographic record
Abstract
This paper argues that while Ontario has witnessed an onslaught of equity and inclusion educational policies aimed at diversifying teacher demographics via “bias-free” hiring practices, teachers across the province do not equitably reflect the identities and demographics of the student population. We the authors refer to this as the teacher diversity gap which refers to the discrepancy in the proportion of racialized teachers to racialized students (Hrabowski & Sanders, 2014; Turner, 2015). Through a literature review and responses of 10 educators interviewed, this article critiques the dominant narrative associated with the benefits of “bias free” hiring practices embedded in equity and inclusive policies (James & Turner, 2017; Ryan 2009) arguing that the teacher workforce has remained predominantly white and not adapting in terms of representation with the increasingly minoritized student population. The authors further argue that bias-free hiring is a prime example of colour-blind racism (Bonilla-Silva, 2006; Zemblyas, 2003) claiming to select candidates based on their individual merit and ability, simultaneously dismissing discussions about systemic racism and other barriers embedded within existing educational policies and practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".