‘You people talk from paper’: Indigenous law, western legalism, and the cultural variability of law’s materials
Bibliographic record
Abstract
Focusing on Delgamuukw v. British Columbia, this paper argues that it is not difficult, if one takes a fair look at Gitxsan and Wet’suwet’en governance, to see legal ways of knowing drawn from specific materials. The materials are different from the ones western law draws on, but that does not render them less legitimately legal. To get beyond prejudices about what counts as law, settler courts may have to begin by admitting that indigenous oral narratives, songs, totem poles and other aspects of material culture are legal materials. Then those who work in those courts would have to interrogate that new knowledge, and perhaps learn that in getting there by that method they ended up bringing colonialism with them. Instead, perhaps we can ask how someone trained as a lawyer in a textual tradition might learn to see that a song, a story, a ceremonial robe, or a totem pole, could be law or legal title rather than evidence of those things. After all, Canadian jurisdiction is as much a story as anything brought forward by the elders who testified in this case. A materialist approach cannot guarantee a better parsing of the problems of communication here, but it may shine a brighter light on what counts as jurisdiction and thus make it more difficult to accept without question that Canada’s courts have the right to decide a case like this.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.039 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".