Decolonizing Self-Determination: Haudenosaunee Passports and Negotiated Sovereignty
Bibliographic record
Abstract
The United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) recognises both Indigenous peoples’ right to self-determination and simultaneously offers protections in regard to states’ right to sovereignty and territorial integrity vis-à-vis Indigenous peoples’ claims. Often, this is considered an internal inconsistency of the UNDRIP, and another common critique is that Indigenous peoples were only recognised as having a diminished right to self-determination, which is less than what everyone else enjoys. This article stands in contrast to these two lines of critique, arguing that the UNDRIP’s articulation of self-determination is potentially ushering in a broadening, and possible reshaping, of self-determination, which has been increasingly decoupled from singular Westphalian notions of ‘sovereignty’ and ‘territoriality’ in ways that require ongoing negotiation between peoples and states. This case study of the Haudenosaunee Confederacy’s issuance and use of their passports, based on original fieldwork including a set of qualitative interviews with key informants, demonstrates how the Haudenosaunee Confederacy is pushing the practice and understanding of self-determination in multiple, new directions to include plural sovereignties in deeply significant ways concerning International Relations in both theory and in practice.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".