Understanding How Minoritized Female Legal Professionals Negotiate Extra-corporate Commitments and Legal Practice
Bibliographic record
Abstract
ABSTRACT This thesis examines the experiences of minoritized female legal professionals working in law and aims to explore how minoritization is reproduced in the legal profession. How lawyers, law clerks and legal assistants navigate legal practice as well as practices in social justice throughout their careers is revealed through professional and personal narrative. The study documents their struggle to “fit” in the profession filling in the everyday/everynight reality of working in law, and reveals there is negotiation at every stage of their careers. Methodologically, I have chosen an anti-racist theoretical analysis as well as Clarke and Smith's theories. Using institutional ethnography, I start with the broad question of what is the lived experience of minoritized women working in corporate law? I employed a mixed method design using interviews to examine the experiences of 12 minoritized female legal professionals, including myself, at various stages in their legal careers. I also analyze certain “texts” used and relied upon by the legal community to understand how they are informed by the ruling relations and how they in turn, inform governance and social relations. The study reveals there is systemic discrimination and disadvantage in the profession and the story of disadvantage for minoritized female legal professionals working in law is one of cumulative inequity and trauma.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".