Redressing the Underrepresentation of Racialized Faculty at an Ontario Polytechnic
Bibliographic record
Abstract
Racialized faculty are underrepresented across Ontario’s colleges of applied arts and technology. This dissertation-in-practice (DiP) uses critical theory and critical race theory to position the underrepresentation of racialized faculty in the delivery of curricula as a problem of practice (PoP) within a Faculty at an Ontario polytechnic. This underrepresentation not only contributes to homogenous teaching and learning but also can create a negative climate for racialized students who do not have the opportunity to see their identities reflected in the faculty teaching them. Racialized faculty also face significant challenges as minorities navigating spaces in which they do not feel a sense of belonging. This DiP advocates for a future state in which racialized faculty representation approaches external availability in the workforce, brought about through a belonging-based recruitment and hiring strategy (BRHS) that infuses equity-mindedness into recruitment, interviewing, and deliberation processes. This change to recruitment and hiring would be implemented through an adaptive leadership and critical allyship approach, following a framework that integrates Kotter’s eight stages of change model with Lawrence’s emerging change model. Dialogue, reflection, and the elevation of racialized voices are key principles for communication throughout the change plan. The plan is monitored and evaluated through an integrative framework centering inclusivity, power, and sustainability. Implementing the BRHS can transform spaces of power and decision making within the institution, as well as create a more inclusive collegial culture, leading to better recruitment and retention of racialized faculty.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.048 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".