The National Self-Represented Litigants Project: Identifying\tand\tMeeting the Needs of Self-Represented Litigants Final Report
Bibliographic record
Abstract
The goal of this qualitative study was to develop data on the experience of self-represented litigants in three Canadian provinces: Alberta, British Columbia and Ontario. Field sites in each province were used as primary data collection points, but SRL respondents also came via social media and from all over each province. In addition, service providers (court staff, duty counsel, pro bono lawyers, staff in community agencies working with SRL’s) were included in the sample. Most respondents (almost 90% of SRL’s and 100% of service providers) participated in an in-depth personal interview; the remaining 10% of SRL’s participated in a focus group.\nData sample 259 SRL’s from the three provinces participated in either an in-depth personal interview or a focus group. Including follow-up interviews, a total of 283 interviews were conducted with SRL’s. In addition 107 interviews were conducted with service providers (defined above).\nSRL demographics The characteristics of the SRL sample are broadly representative of the general Canadian population. 50% were men and 50% were women. 50% had a university degree. 57% reported income of less than $50,000 a year and 40% (the largest single group) reported incomes of less than $30,000 a year. 60% of the SRL were family litigants and 31% were litigants in civil court (13% in small claims and 18% in general civil). 4% were appearing in tribunals (the remainder were unassigned). The majority of family SRL’s were filed in the divorce court (Supreme Court, Queen’s Bench or Superior Court) and a smaller number in provincial family court.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".