Mobile and Web-Based Legal Apps: Opportunities, Risks and Information Gaps
Bibliographic record
Abstract
Mobile and web-based apps are one technology with the potential to improve access to justice, either by helping lawyers increase the efficiency of service delivery or by reducing the need for recourse to lawyers altogether for some legal needs. Notwithstanding growing excitement about the potential presented by legal apps, there has been no comprehensive study regarding the range of such apps currently available to Canadians, nor has there been a concrete exploration of what these apps purport to do and whether they have the capacity to actually improve access to justice. In this paper, we offer a preliminary taxonomy of the legal apps available in Canada, of which we have identified approximately 50. This taxonomy seeks to identify developers, targeted users and the functions that legal apps are designed to perform. Further, we contribute to future policy discussions about legal apps through an analysis of the potential benefits and risks of using this technology in the pursuit of access to justice. Finally, we conclude with a call for dedicated empirical data and research on legal apps in Canada and for increased policy attention to leveraging the opportunities and mitigating the risks presented by legal apps.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".