Bibliographic record
Abstract
Despite increased public dialogue about the need for inclusion, marginalized lawyers adjust their behavior to “fit” in their legal workplaces. In this article, the author presents the results of interviews with lawyers in Canada who self-identify as belonging to a marginalized group based on race, ethnicity, Indigeneity, gender or sexual identity, working-class background, and/or disability. Based on these interviews, the author advances a taxonomy of the five strategies employed by these lawyers to fit in to their workplaces: covering strategies, compensating strategies, mythologizing strategies, passing strategies, and exiting strategies.Marginalized lawyers employ covering strategies, which may be appearance, affiliation, advocacy, or association-based, to hide or minimize characteristics that may distinguish the individual from the dominant group. Compensating strategies include the individual’s efforts to work harder, obtain more credentials, maximize their social capital, be perfect, and take on extra diversity work. Each of these techniques is designed to “make-up” for the perceived failure of being a marginalized lawyer. Mythologizing strategies involve creating internal narratives to reduce the effects of discrimination. Marginalized lawyers use passing strategies to censor various aspects of themselves in an attempt to be perceived as a part of the dominant group. Finally, exiting strategies are a last resort; the marginalized lawyer leaves their workplace or limits their legal work to specific firms or areas of law.The author argues that developing a taxonomy of strategies facilitates understanding about how law firms and lawyers may strategize to create more inclusive work environments. The author also clarifies the extent to which we require some marginalized lawyers to adjust aspects of who they are so that they can survive in their workplaces.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".