ACCESS TO JUSTICE FOR ALL: TOWARDS AN “EXPANSIVE VISION” OF JUSTICE AND TECHNOLOGY
Bibliographic record
Abstract
In this paper, the authors examine developments in the Canadian access to justice dialogue from Macdonald’s seminal 2005 analysis to the recent reports of the National Action Committee on Access to Justice in Civil and Family Matters [NAC]. They draw on the NAC’s call for an “expansive vision” of access to justice as the basis for critically evaluating examples of particular technologies used or proposed as responses to the access to justice crisis in Canada. In so doing, they illustrate the importance of conscious consideration of deliverables and beneficiaries in prioritizing technologies for deployment, in determining how the technology ought to be deployed, and in evaluating the potential of a technology to facilitate access to justice. The authors argue that nuanced accounts of the relationships between justice deliverables, technological mechanisms for delivery and intended justice beneficiaries are essential to developing good decision-making mechanisms with respect to access to justice and technology.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.024 | 0.145 |
| Scholarly communication | 0.038 | 0.029 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.012 | 0.019 |
| 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".