Reducing the “Justice Gap” Through Data for Systemic Change: Using Multiple-Perspective Legal-Needs Surveys to Improve Person-Centered Justice
Bibliographic record
Abstract
Abstract Recognizing the justice data deficit across Canada, we undertook a multi-faceted project to better understand access to justice (A2J) issues and legal needs of individuals and communities in Saskatchewan. This paper describes the 2021-2022 Saskatchewan Legal Needs Survey, a multiple perspective service provider legal-needs survey intended to complement user-centred surveys and designed to capture the experiences of justice system users via perceptions of service providers. Comprised of two online self-report questionnaires (Community Agency Survey and Lawyer Survey), data were collected from a provincially representative sample of community agencies (n = 67) and lawyers (n = 272). Results generally highlight respondents’ perceptions of A2J issues and priority legal needs based on their experiences with the communities and clients they serve. Overall, a multiple perspective service provider approach affords greater insight into justice system gaps and serves as a viable model for future person-centered justice data collection projects, nationally and internationally.
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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 | 0.066 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".