The Cost of Uncertainty: Navigating the Boundary Between Legal Information and Legal Services in the Access to Justice Sector
Bibliographic record
Abstract
The self-regulatory bodies that oversee legal professionals in Canada maintain strict control on the delivery of legal services, and access to justice projects must therefore always be conscious of activities that would violate certain restrictions. Careful adherence to these parameters is made difficult, however, by the lack of clarity about where the relevant boundaries are drawn. Using a project that provides legal assistance for refugees as a case study, this article highlights the challenges that the unclear distinction between “legal information” and “legal services” creates for access to justice initiatives. We conclude that the uncertainty can carry a variety of significant costs—including financial expense, human resource burdens, and unnecessary limits on program innovation—in a sector where affordable and creative solutions are desperately needed as a result of a persistent access to justice crisis. Ultimately, it is not merely the under-resourced access to justice sector that bears these costs, but rather disadvantaged individuals and society as a whole.
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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 | 0.032 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.048 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".