Informal Justice: An Examination of Why Ontarians Do Not Seek Legal Advice
Bibliographic record
Abstract
Modern access to justice scholarship takes as its premise that the focus of legal reform must be on the legal problems experienced in the day-to-day lives of the public; not just those problems that are brought before the formal court system for adjudication. In 2014, the Canadian Forum on Civil Justice [CFCJ] completed a comprehensive survey for the Cost of Justice Project inquiring into the civil legal needs among ordinary Canadians. One of the many conclusions that can be drawn from the survey data is the finding that most Ontarians do not go to lawyers in order to resolve their legal problems. Ontarians, rather, tend to engage in methods of resolution that can be categorized as informal self-help methods. This paper explores possible reasons why Ontarians do not seek out formal legal advice when they experience a legal problem. It examines various factors that may influence Ontarians’ decision not to seek formal legal advice including the respondents’ income level, their perception of the law and the category of legal problem experienced. The paper concludes that most Ontarians seek to resolve their legal problems through informal self-help methods, not because of their inability to afford legal services, but rather because of how legal problems are perceived. This work will provide insight into why most legal problems do not end up before the formal legal system, which will be of significance to policy makers who desire to make meaningful and inclusive reforms to the justice system.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".