A FLEX TIME JD: NEW APPROACHES TO THE ACCESSIBILITY OF LEGAL EDUCATION
Bibliographic record
Abstract
This article examines accessibility and inclusion in legal education. Responding to the Canadian Bar Association’s call for accessible and innovative legal education in the Futures Report, this study explores the possibilities (and limits) of a Flex Time Juris Doctor (“JD”) program and how such a program might foster further diverse and inclusive learning community for law students. The article situates the debate around more flexible forms of legal education in historical context, highlighting the role part-time legal studies has played in facilitating the entry of outsider groups into the legal profession. While there is not a mid-sized city in the US without part-time law school programs, intentionally designed to accommodate flexible legal education, Canada’s law schools remain premised on full time JD studies. A Flex Time JD responds to a variety of the challenges now facing Canadian legal education, from financial accessibility at a time of significantly rising tuition and law student debt, to the integration of technology enhanced pedagogy. Drawing on the research gathered in Osgoode Hall Law School’s Accessible JD Working Group over the last two years, including surveys of prospective law students, the article canvasses the potential features of a Flex Time legal education program, and barriers to its implementation. The authors conclude that if a Flex Time JD model has the capacity to meet the diversity, family status, ability and financial needs of present and future lawyers, then it has the potential to enhance both the accessibility and quality of legal education.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".