The Politics of Indigenous Consent: Negotiating the Fields of Possibility for Self-Determination
Bibliographic record
Abstract
This research project aims to explore Indigenous consent processes in emerging lithium frontiers in Quebec, Canada and Copiapó, Chile. Combining the Yellowhead Institute’s (2019) spectrum of Indigenous consent and Gaventa’s (2006) continuum of participatory spaces as a guiding framework, I conduct a multi-sited critical discourse analysis examining both dominant state-led frameworks governing Indigenous consultation and lithium development, and counter-discourses represented in statements published by Algonquin Anishinaabeg and Colla Indigenous communities contesting proposed lithium mining projects. My findings revealed that green extractivist discourses function in different ways to restrict the states’ already limited spaces of Indigenous recognition and consultation. In the face of such limitations, these Indigenous communities are articulating conceptions of consent that simultaneously use and refuse state frameworks of recognition. This analysis demonstrates how contested spaces of participation along the spectrum of consent reflect the reality of “nested sovereignties” - where Indigenous communities assert and enforce their consent both within and against the state. This project contributes to the broader discussion exploring how Indigenous jurisdiction complicates andtransforms the extractive frontiers of the energy transition.
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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 | 0.043 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.140 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".