Sex Discrimination, Assimilation, and Austerity: The Untold Story of Canada’s Indian Act, 1975-1985
Bibliographic record
Abstract
This article is about the misunderstood history of a Canadian law for determining Indian status, the decades-long struggle to remedy sex discrimination in the law, and the significance of judges writing history. Since before Canada’s confederation until amendments to the Indian Act in 1985, Indian women, unlike Indian men, lost their Indian status if they married non-Indians. Even with the 1985 amendments, the law still disadvantaged people who traced their Indian status along the female line. Facing a challenge to the law based on sex discrimination, the government argued that the enduring disadvantage to women was the only way to reconcile the equality rights of Indigenous women and the self-governance rights of Indigenous communities. The government’s account of the legislation’s history has been widely accepted by scholars and confirmed in case law. Through scrutiny of newly declassified government records, this article refutes the government’s claim that the 1985 Indian Act amendments were the product of a necessary compromise between competing Indigenous rights claims. Rather, the government used controversies it had provoked about Indigenous self-governance to obscure a legislative objective shared by both Liberal and Conservative governments: to minimize the number of status Indians and thereby shrink the population entitled to federal benefits, weaken Indigenous land claims, and ultimately undermine Indigenous self-governance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.069 | 0.042 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| 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".