Foreign Trained Black Lawyers’ Experiences in Ontario: I am a Lawyer! Am I a Lawyer? An Odyssey!
Bibliographic record
Abstract
Abstract This thesis examines the experiences of foreign trained black lawyers navigating the Canadian legal credentialing process in Ontario and how that has impacted their lives. I chose to focus on foreign trained black lawyers (FTBL) and take a comparative look at black lawyers born and trained in Africa and the Caribbean as well as Canadian born blacks who studied outside Canada. FTBL includes those who are born in Canada and study abroad and those who are immigrants to Canada. The research employed a discursive framework, using the intersectionality of critical race theory and integrative anti-racist theory to contextualize and examine the issue. I employed a mixed method design using an online survey and interviews to examine and analyze the challenges of foreign trained black lawyers becoming credentialed in the Ontario legal system. The five major themes that emerged are as follows: 1) the NCA’s (National Committee on Accreditation) lack of transparency and consistency in the process of accreditation, and the Law Society of Upper Canada, specifically the LSUC exams; 2) Articling in Ontario, focusing on the challenges of securing articling positions; 3) the Impact of Racism, focusing on disempowerment and discrimination;4) Resilience and Career motivation, highlighting the participants’ coping mechanisms, sustenance and support; and 5) Networking, in particular relating to the importance and relevance of social capital in finding suitable work experiences.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".