Indigenizing Human Rights: First Nations, Self-Determination, and Cultural Identity
Bibliographic record
Abstract
In this paper I argue that with regard to Indigeneity, there is a profound irony at work in the purportedly emancipatory power of human rights discourse. On the one hand, it is clear that this discourse has made, and continues to make, a significant contribution in supporting Indigenous peoples to articulate their claims for cultural recognition and struggle for freedom and self-rule. On the other hand, I contend that this discourse has a detrimental effect: a Trojan horse of sorts. The discourse of rights brings along with it powerful theoretical commitments, assumptions, and institutions that are at odds with and that undermine what most Native communities in Canada take to be an essential component of their cultural identities and self-understandings. I contend that at the heart of this issue are profound philosophical tensions about notions such as the self, community, and agency. That is, the core tensions rest on ideas that most Canadians take to be basic or unquestionable. I show that such assumptions are not only not basic or unquestionable, but, in fact, grounded in Western liberal ideals. I argue that dialogue in this context requires understanding that human rights, or any form of a global ethic, needs to be recast within Indigenous self-understandings and beliefs if such a dialogue is to be authentically intercultural and fruitful.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".