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Record W3210892814

A Cielo Abierto: Constellations for Extraterritorial Multinational Corporate Accountability for Environmental Damage in Human Rights Law

2021· article· en· W3210892814 on OpenAlexaboutno aff
Astghik Hairapetian

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationAccountabilityHuman rightsLawConstellationPolitical scienceBusiness
DOInot available

Abstract

fetched live from OpenAlex

Human rights abuses resulting from Canadian mining operations in Latin America have increasingly surfaced, and new, bold paths of accountability are being forged.However, this is not the end.Few regulations exist for mine closure, a process that itself can leave devastation in the communities affected.In this Comment, I ground my analysis in the facts and history of the Marlin Mine in western Guatemala.I set forth the structural barriers to justice posed by multinational corporations with operations abroad, and discuss two possible routes for accountability in relation to mine closure.First, within the universal human rights system, the International Covenant on Economic, Social, and Cultural Rights * J.D., UCLA School of Law.My gratitude to Charis Kamphuis for the unwavering encouragement, and all my Justice and Corporate Accountability Project colleagues for their valuable feedback.Sincere thanks to the editors of Loyola of Los Angeles International and Comparative Law Review for providing thoughtful edits and support.1. Similarly, rather than lie on your back, in Spanish you lie boca arriba, or mouth up.In both instances you find yourself in a vulnerable position; in the second, however, you might have something to say about it.2.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.032
Scholarly communication0.0160.010
Open science0.0010.007
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.030
GPT teacher head0.252
Teacher spread0.223 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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