Measuring Improvements in Access to Justice: Utilizing an A2J Measurement Framework for Comparative Justice Data Collection and Program Evaluation Across Canada
Bibliographic record
Abstract
Improving access to justice in Canada’s justice system is often the impetus for introducing new innovations or changing existing systems. However, measuring the effectiveness of these initiatives to improve access to justice is challenging without a common language to help identify and define the elements of access to justice, and without a common framework to help guide the measurement and evaluation of whether improvements are being realized. This paper seeks to contribute to the access to justice measurement discourse by highlighting an access to justice evaluation framework that has been developed with the triple aim objectives of improving population access to justice, improving user experience of access to justice, and improving costs. We also demonstrate how this framework has been used as part of the planning and evaluation of the Listen Project in Saskatchewan, illustrating how this framework can be universally adapted to other projects and initiatives throughout the justice sector.
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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 | 0.199 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.045 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".