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Record W4256709334 · doi:10.1093/yiel/yvab020

C. Argentina

2020· article· en· W4256709334 on OpenAlexaboutno aff
María Florencia Saulino

Bibliographic record

VenueYearbook of International Environmental Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceConventionIndigenousLegislatureHuman rightsLawMontreal ProtocolLatin AmericansScope (computer science)Environmental protectionPublic administrationEnvironmental planningEnvironmental resource managementGeographyOzone layerEnvironmental science

Abstract

fetched live from OpenAlex

This report presents the most important environmental legislative and judicial developments in Argentina during the past year. The report will first present developments in the signing and implementation of international treaties. Last year, Argentina signed the Regional Agreement on Access to Information, Public Participation and Justice in Environmental Matters in Latin America and the Caribbean; made a new commitment to reduce its greenhouse gas emissions and improve its adaptation capacities; and carried out various projects to mitigate and adapt to the effects of climate change. It also introduced regulations aimed at implementing the commitments assumed under the Kigali Amendment to the Montreal Protocol on Substances That Deplete the Ozone Layer (Montreal Protocol) and under the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and Their Disposal (Basel Convention). Second, the report will refer to the judgment of the Inter-American Court of Human Rights (IACtHR) in Indigenous Communities Members of the Lhaka Honhat Association (Our Land) v Argentina. In this case, the court held Argentina responsible for the violation of the rights to cultural identity, a healthy environment, adequate nutrition, and access to water. This case is particularly relevant because it is the first time that the IACtHR has addressed the scope of the right to a healthy environment in a contentious case.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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 teacher head, not a consensus.

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
Published2020
Admission routes1
Has abstractyes

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