Career Wellbeing Among Racialized Lawyers in Canadian Law Firms
Bibliographic record
Abstract
The upward mobility of racialized lawyers within Canadian law firms continues to lag behind that of their White counterparts. While some progress has been made in recruitment, significant disparities remain in retention and promotion. This qualitative study explores how racialized lawyers experience working in Canadian law firms and how these experiences impact their career wellbeing. Semi-structured interviews were conducted with 19 racialized lawyers, and the data were analyzed using reflexive thematic analysis. The themes identified were informed by existing literature on organizational dynamics and professional inequality and included tokenism processes (hypervisibility, role encapsulation and boundary heightening), bias (status expectations and homophily preferences) and organizational norms (professional and emotion), as well as several subthemes. These themes and subthemes were linked to diminished access to meaningful work, positive relationships with colleagues, career growth opportunities, perceived organizational support and autonomy—all core dimensions of career wellbeing. The findings reveal the cumulative psychological and structural burdens racialized lawyers face and underscore the need to embed equity into law firm culture in ways that meaningfully support career wellbeing, particularly amid increasing sociopolitical resistance to DEI efforts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".