Bibliographic record
Abstract
It is well documented that mining activities can have disproportionate impacts on Indigenous communities. While states and industries are increasingly recognizing the participatory rights of Indigenous Peoples, adapting their practices to new standards, a growing literature shows there is good reason to be skeptical about their capacity to foster meaningful Indigenous participation. This chapter focuses on how Indigenous Peoples negotiate mining development inside and outside official regulatory processes in Brazil and Canada. Both countries manage Indigenous rights—both claimed and/or recognized—while facing an increase in investment on major projects located on or close to Indigenous lands. While Indigenous Peoples and other actors have focused on consultation and free, prior, and informed consent (FPIC) rights in Latin America, in Canada, court decisions and practices have promoted “benefit-sharing” approaches. Drawing on the “inhabited institutions” theoretical approach, we will focus on the interpretation, negotiation, and contestation of institutions that emerge from the interactions among Indigenous Peoples, mining industries, and governments. In doing so, we will show that institutions allow certain groups disproportionate access to the decision-making process. Nevertheless, Indigenous Peoples are asserting their political agency, creating new ways to interpret their participatory rights, and challenging how the state defines their rights, thereby creating new space for negotiations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".