Bibliographic record
Abstract
In September 2018 the University of Victoria Faculty of Law on Vancouver Island, Canada welcomed its first cohort of students to its cutting edge and innovative joint degree programme in Canadian Common Law (Juris Doctor (JD)) and Indigenous Legal Orders (Juris Indigenarum Doctor (JID)). The JD/JID programme draws on the law faculty’s more than two decades of experience and research on Indigenous legal orders, and Indigenous legal education. It is the first of its kind in the world, combining intensive study of Canadian Common Law with rigorous engagement with Indigenous law. The rationale behind this programme is to engage with Indigenous legal orders using the depth, rigour, and critical focus that law schools bring to the study of other legal orders. Pushing against exclusion happening in higher education throughout the Commonwealth and beyond, the JD/JID programme aims to ensure that education in Indigenous Law is no longer an education in exclusion and displacement. This short piece provides necessary background to the programme, including structure and content, and details its transsystemic pedagogical and community-based learning approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".