Cuestionando el consentimiento en las Cortes: una crítica socio-legal a los acuerdos indígenas-industria
Bibliographic record
Abstract
In this article we argue that the legal and social contexts that typically inform the formation of Indigenous-industry agreements in Latin America are marked by enormous power disparities and stark epistemological differences. The literature reviewed here supports the conclusion that it is likely that many of these “agreements” lack legitimacy, and even legality. This in turn raises serious questions about whether or not agreements formed under current conditions could possibly rest on any meaningful notion of consent. We make this important point in order to focus on a narrower set of questions, of the present but also very much one of the future, as we face the aftermath in the years and decades to come, of the proliferation of agreements under present circumstances. What happens when a community mobilizes in order to challenge the legality of an agreement signed with a company in the extractive sector, contesting the idea that it actually consented? What happens if the company and / or the State present a document with signatures of former community leaders that allegedly represent consent? Finally, if the company and the state are unresponsive to a community’s concerns about the deal, can the community resort to the courts? In this article we examine some of these issues by referring to Peru as a case study, and in conclusion we analyze their significance for ongoing normative developments in relation to Indigenous peoples’ right to free, prior and informed consultation and consent.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| 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".