"""I smooth'd him up with fair words"": Intersocietal law, from fur trade to treaty"
Bibliographic record
Abstract
History is an essential part of aboriginal law. The two disciplines, however, may produce incompatible narratives of indigenous-settler relations. In addition, indigenous legal traditions and the fur trade in the old North West have been under-represented in Canadian legal history, a gap that demotes over two centuries of working relationships to a brief preface to the numbered treaties and confederation. This dissertation seeks to bring under-observed normative relations between indigenous and European traders into Canadian legal history. It further considers the relevance of fur trade law to the jurisprudence on aboriginal treaty rights and the significance of history in overcoming historical injustice in settler states. Using an ethnohistorical methodology, three case studies are presented on the law of the fur trade followed by a chapter connecting the interpretation of the intersocietal law of the fur trade to the interpretation of treaties in history and law. Focussing the fur trade as conducted by the Hudson's Bay Company and the North West Company, the case studies investigate the normative expectations of the indigenous and company traders around particular aspects of the trading relationship. These aspects include institutions of leadership, the formation and maintenance of friendships, negotiations of trading post location, and the exchange of provisions and support in times of famine and illness. In these case studies, the intersocietal law of the trade is interpreted as incomplete and often laden with misunderstanding. It involved competition between normative systems and harboured persistent disagreements, even while sufficient shared obligations and occasional shared meanings emerged to support robust working relationships. This interpretation of the intersocietal law of the fur trade demands a shift in the characterization of treaties in history and law. I argue that to better serve the aims of justice and reconciliation, both the classification of treaties in history and the interpretive focus of the treaty rights jurisprudence must change to allow the complexity of the historical relationship - including the disagreements and injustices buried in simpler narratives - to emerge.
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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 | 0.003 | 0.005 |
| 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 | 0.046 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".