The Shifting Frontiers of Law: Access to Justice and Underemployment in the Legal Profession
Bibliographic record
Abstract
The article examines two interrelated issues attracting attention from the legal academy, the profession, and policy makers: i) the crisis of access to justice among ordinary Canadians, and ii) the increasing number of qualified and underemployed lawyers. This article sets out to understand the interrelated factors underlying these two trends, and explores long-term, accessible solutions to address the misalignment between the supply of underemployed law graduates and a demand for affordable legal services. In response to these twin problems, we examine how legislative reform, open source networks, and the automation of legal work can allow lawyers to create more cost-effective delivery mechanisms for legal services, while allowing clients to choose, and work with, lawyers in a more informed manner. While the alternatives we explore are a radical shift from the traditional methods of the legal profession, they are in line with emerging technological realities, and are realistic market solutions to the access to justice problem. To conclude, we focus on the legal academy’s important role in motivating budding lawyers to think critically about how the legal profession, as a social institution, can be ameliorated to ensure that claims for justice do not fall outside of its purview.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".