Pragmatic Assorted Strategies: How Canadian Cause Lawyers Contribute to Social Change
Bibliographic record
Abstract
Public interest litigation often involves working with cause lawyers (i.e., those who work with and support social causes), which are understudied in Canada. This paper synthesizes some initial results, themes and issues from over 30 semi-structured qualitative interviews with Canadian cause lawyers regarding how they consider and use assorted strategies to attain their goals. As cause lawyering encompasses a significant breadth of potential activities and issues, this research focuses on lawyers who met three combined criteria: working for disadvantaged groups to improve their status quo in systemic ways; using legal skills in some broad way, including outside formal practice; and compensation not being the main driver for their work. The lawyers were based in private law firms, clinics, other non-governmental organizations and academia, and their work supported a variety of causes and specific issues. The interviewees illustrate how Canadian cause lawyers are pragmatic and strategic about using diverse methods to achieve their objectives. For example, the lawyers employ combinations of traditional public interest litigation, other litigation and summary advice, law reform, education and capacity building, media advocacy, and community engagement and work. The actual mixture for each lawyer varies depending on the lawyer's role, issue context and opportunities, organizational focus and structure, and other factors. Administrative work and resource constraints also have important impacts, particularly given the nature of cause lawyering work and context. These approaches are consistent with lawyers being realistic about the significance and effectiveness of law, including viewing rights as important but contingent political resources rather than mythologizing them. They also reinforce the constitutive role of law in society, including how law and society impact each other. The research thus provides key insights into how cause lawyers use such perspectives and tools to contribute successfully to social change.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.067 | 0.047 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".