El Riesgo Financiero Y El Consentimiento Indígena (Financial Risk and Indigenous Consent)
Bibliographic record
Abstract
The government agency that supervises financial institutions in Peru announced in December 2013 that it would promulgate rules on assessing risks associated with social conflicts surrounding mining. The wisdom of this approach was confirmed by the findings of a Harvard University report on the high costs of social conflicts associated with mining, released in May 2014. In the article below, we describe how the International Financial Corporation of the World Bank, and the 77 global financial institutions who have signed on to the Equator Principles, have come to the conclusion that social conflict with indigenous communities needs to be resolved through the application of free, prior, informed, consent. While the requirement to obtain consent of an indigenous community would appear to make it more difficult for extractive industry projects to proceed, theories of dispute resolution developed by the Harvard Negotiation Project suggest that where consent is obtained, it is more likely that conflicts will be reduced. This article concludes with the observation that the banks have concluded that the benefits that would result from reduced social conflict in cases where consent was obtained, outweighed the disadvantages of the possibility that some projects would not be able to go ahead for lack of 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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".