Bibliographic record
Abstract
Canada’s reconciliation with Indigenous peoples and groups in Canada is an ambitious goal with little in the way of clear direction. Canadian courts have provided limited direction in their decisions, yet the result of litigation has imposed a concept of reconciliation based on First Nations remaining subordinate to state authority and interests. Reconciliation will be confounded without gaining a shared understanding with Indigenous peoples. Different Indigenous groups will have their own interpretation of what reconciliation may require to be successful. One approach to seeking common understandings is for Canadians to learnhow relationality operates as a function of disparate Indigenous legal orders. While substantive research into Indigenous legal orders is relatively new in Canadian scholarship, there is much knowledge to be gleaned from interdisciplinary research, particularly in anthropology, from the early twentieth century. At the risk of presenting an abrupt shift in disciplinary paradigms in this article, the author follows a thread of relationality from Canadian courts through the lens of doctrinal jurisprudence into relationality within various Indigenous legal orders through anthropological study. Combined, the article offers a potential path to reconciliation through relationality within Indigenous legal orders.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".