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Record W1886219555

El Riesgo Financiero Y El Consentimiento Indígena (Financial Risk and Indigenous Consent)

2014· article· es· W1886219555 on OpenAlexaff
Shin Imai, Sally Kang

Bibliographic record

VenueeYLS (Yale Law School) · 2014
Typearticle
Languagees
FieldEnvironmental Science
TopicPublic Health and Environmental Issues
Canadian institutionsYork University
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.006

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.

Opus teacher head0.010
GPT teacher head0.249
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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