Turbulent Transitions: Implementing Global Perspectives in Legal Education
Bibliographic record
Abstract
Throughout Globalisation and Legal Scholarship, William Twining often mentions ‘legal scholarship’ and ‘legal education’ in the same breath, without discussing the practical and institutional challenges that academic lawyers may face if they are to meaningfully carry global perspectives into their legal pedagogy. Bridging Twining's work with the pedagogical work of Harry Arthurs and the theoretical work of Boaventura de Sousa Santos, the present paper illuminates tensions within the legal academy that may stand in opposition to the globally informed theoretical challenges to Western understandings of law, and the competing conceptions of globalisation which vie for supremacy in Canadian law faculties. The author argues that the adoption of global perspectives in the Canadian specialist's legal scholarship (as advocated by Twining) does not provide for a seamless transition into legal education, but that these challenges should be confronted rather than capitulated to.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".