Legal Mobilization and Democracy: An Analysis of Interventions at The Supreme Court of Canada, 2013-2021
Bibliographic record
Abstract
When the Constitution was patriated in 1982, which included the introduction of the Canadian Charter of Rights and Freedoms (Charter), the Supreme Court of Canada (SCC) inherited a new importance in shaping law and public policy. Following this development, Canadian Civil Society Organizations (CSOs) became more involved in “legal mobilization”—the strategic use of law and courts to promote social and political change. Most commonly, organizations “intervene” in the SCC, a process where they are sometimes entitled to make brief arguments before the Court in cases in which they are not direct parties. Situated in a longstanding debate in Canadian political science, this dissertation asks: to what extent does intervention enhance or damage democracy?Guided by the three democratic benchmarks of inclusivity, participation, and responsiveness, this dissertation provides a comprehensive, empirical analysis of the democratic implications of intervention at the SCC. In the process of addressing these benchmarks, this dissertation makes a major methodological contribution to the field of public law: it is the first, in-depth, multi-method evaluation of intervention in Canada. The study examines over 600 interventions across 103 Charter cases heard by the SCC between 2013 and 2021. The quantitative portion of the project includes bivariate regression analysis, quantitative content analysis and statistical text matching. The qualitative portion involves 23 semi-structured interviews conducted with CSO professionals, repeat counsel, and former law clerks, as well as a qualitative content analysis of interest group and law firm documentation, written judicial opinions, and written arguments submitted by interveners.\nThe core argument of this dissertation is that while intervention has certain democratizing features, in practice, intervention exhibits several challenges for Canadian democracy, or what I call “representation gaps.” First, the study finds that the economy of repeat interveners, and the “Supreme Court Bar,” is not reflective and inclusive of the plurality of interests that exist across Canadian society. Second, through the process of intervening, a small network of lawyers act as gatekeepers for which cases and voices are worthy of participation. And third, the Court is not responsive to all intervening actors. Rather, the Court engages with those who are most familiar with the “rules of the game”: governments and legal organizations.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".