Measuring Access to Civil Justice: An Empirical Study of Ontario's Reform Initiatives
Bibliographic record
Abstract
Access to civil justice remains one of the most pressing concerns within the legal community in Canada. Yet, despite over a half century of reform efforts, many people still struggle to resolve their legal difficulties in a timely and cost effective manner. Part of the reason that reform efforts have yet to solve this crisis is that scholarship has only recently begun to investigate possible measures that can evaluate whether programs and initiatives have positively impacted the ability of ordinary Canadians to resolve their legal problems. The primary purpose of this dissertation is to support the development of such measures and it contributes to this work in three ways. First, it situates the access to civil justice conversation within a theoretical framework in order to define what is being measured. The dissertation asserts that John Rawls' theory of justice as fairness is an appropriate conception of justice for a pluralistic democracy. Applying this theory, in conjunction with Lesley Jacobs' three dimensional model of equal opportunities, this dissertation identifies three measures of justice for assessing the impact of programs and initiatives: procedural fairness, background fairness, and stake's fairness. Second it takes seriously the need to include public perceptions of justice into policy development by examining hundreds of conversations about legal problems that are posted to the social media website Reddit. Engaging in both a quantitative and qualitative content analysis of this data, this dissertation identifies common themes about how these individuals understand and interact with their legal problems. It explains how these themes can in turn act as benchmarks to assess the efficacy of access to justice initiatives. Finally, the dissertation notes that people commonly use Reddit to crowd source both legal research and legal advice. It argues that despite legitimate concerns, when assessed against a justice as fairness measurement framework, both of these methods of resolution have a positive impact on improving access to civil justice.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".