Mobile and Web-Based Legal Apps: Opportunities, Risks and Information Gaps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it