The Power of Legal and Historical Fiction(s): The Daniels Decision and the Enduring Influence of Colonial Ideology
Bibliographic record
Abstract
It’s been several months since the Supreme Court of Canada (SCC) rendered its judgment in Daniels v. Canada (2016), affirming that the term “Indian” in s. 91(24) of the Constitution Act (1867) includes Métis and Non-Status Indians. There is a general hope that the decision marks a turning point for Métis and Non-Status Indians within Canada’s colonial structures. I’m not certain this optimism is justified. The judgment was reached based on the types of historical evidence presented and, consequently, there are a couple of statements within the written judgment that give me pause to question how the evidence regarding the histories of Métis and Non-Status Indians were presented to, and then interpreted by, the justices. Bearing in mind that the crux of the case rested on the linguistic meaning and evolution of the term “Indian” in Canadian society through law and policy, evidence was introduced about how the term was used at various points in the past, as well as the context of that usage in order to demonstrate the evolution of a Canadian legal and historical fiction that increasingly restricted the idea of what an Indian was. What the SCC did with the Daniels Decision is reverse that restrictive trend for Indians while constructing new problems.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.042 | 0.114 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.015 |
| 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".