Nurturing Commitment in the Legal Profession: Student Experiences with the Osgoode Public Interest Requirement
Bibliographic record
Abstract
“Eye-opening,” “disheartening,” and “inspiring” are some of the words used by law students who met in 2008–2009 to discuss their mosaic of experience in the field doing public interest work. These students had returned from placements under the first mandatory public interest requirement to be introduced in a Canadian law school (the Osgoode Public Interest Requirement, OPIR). OPIR arose from questions about the relationship between what is learned in law school and what is required to be a professional. Academics have challenged each other to do more to instill an “ethos of professionalism” during law school. Others have suggested that law students who do not receive exposure to the world outside the walls of the law school carry an “idealized conception of the profession” and are often unaware of the many practice contexts available to them. Others have warned that if ethical and professional responsibilities are not modeled and articulated for students, that teaching only the “law of lawyering” does not prepare students for becoming ethical lawyers. Teacher-educator Lee Shulman has bluntly accused law schools of “failing miserably” at connecting its lessons in how to “think like a lawyer” with how to “act like a lawyer.” For years, there have been similar concerns raised about the decline of professionalism among lawyers, both in Canada and in the U.S. A survey of Osgoode graduates revealed that students wanted more opportunities to engage with the community and to experience non-traditional forms of law practice. Osgoode Hall Law School grappled with many of these questions, and in 2007 it approved changes to the curriculum, including a new first year Ethics course (Ethical Lawyering in a Global Community, ELGC) and OPIR. In addition to the more traditional first year mandatory course load, Osgoode Hall law students must also complete ELGC, a minimum of 40 hours of public interest work and then engage in a discussion or written exercise reflecting on their experiences. These reflections are a valuable lens for seeing the profession and the administration of justice through the eyes of first and second year law students. Their experiences remind us in the profession that learning can flow in both directions.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.036 | 0.019 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".