The Distinctive Nature of Academic Integrity in Graduate Legal Education
Bibliographic record
Abstract
Abstract This chapter examines the distinctive nature of academic integrity in graduate legal education in Canada, a nature rooted in the fact that almost all graduate students in law have practiced law. I consider the general acceptance of the unattributed copying of others’ writing within the legal profession and the judiciary, contrasting that tolerance―even approval―with the unsympathetic reception given the same practices in the academy. I then turn to graduate legal education in common law Canada and the diversity among graduate students in law, including significant differences in their undergraduate legal education. Then, because many of the graduate students who have practiced outside Canada want to be admitted to practice law in Canada, I look at the impact that academic misconduct may have on their ability to be admitted to practice. In order to do so, I review all published Canadian court and tribunal admission decisions that considered academic misconduct committed while in law school. Lastly, in light of unique challenges of graduate legal education, I offer some suggestions for preventing academic misconduct and facilitating students’ engagement with their own scholarship.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".