Bibliographic record
Abstract
Abstract This chapter highlights how the clinical work of Érick Sullivan and Fannie Lafontaine from Canada's Laval University with law students supported both the Hissène Habré trial as well as student engagement with international criminal justice. During the 5 years that lasted Laval University's involvement in the Habré case, academics and students alongside practitioners undertook numerous activities. This collaboration demonstrated how legal clinics can be used as a hub bridging academia and community in the context of strategic litigation. Through them, researchers' outputs are feeding strategic litigators, and practitioners' applied knowledge is feeding the researchers' agenda, thus allowing both to co-create innovative arguments that will be used in strategic litigation to achieve the expected long-lasting impact on law and society. This novel partnership structure hosted at Laval University links academic researchers, legal clinics, and NGO partners across Canada to work on an interdisciplinary research program that has both international and Canadian dimensions comprised of three main axes that pertain to different and complementary routes that victims of international crimes can take in Canada, in other states, and before international institutions, to seek criminal, civil, and administrative or other remedies.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".