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Record W4389233244 · doi:10.26443/firr.v14i1.155

Lessons from Montréal

2023· article· en· W4389233244 on OpenAlexaffvenueabout
Aden Gil

Bibliographic record

VenueFlux International Relations Review · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Policies and Emissions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRatificationKyoto ProtocolProtocol (science)Montreal ProtocolLegislatureOzone layerLegislationClimate changeBusinessInternational tradePolitical scienceGeographyLawPoliticsMeteorologyEcology

Abstract

fetched live from OpenAlex

The 1986 Montréal Protocol was an unprecedented success in the United Nations campaign to tackle climate change. The Protocol was the first piece of UN legislation to achieve universal ratification and successfully stopped the deterioration of the ozone layer. This paper analyzes how two key factors - consumer action and a narrow legislative focus - allowed for the Protocol to successfully regulate multi- national corporations at a global level. Further, the paper discusses the failures of the Kyoto Protocol and Paris Accords when considering the capacity in which the protocols regulate corporations and induce significant environmental change, and last, provides recommendations for future UN climate negotiators.
 
 
 

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.082
Threshold uncertainty score0.684

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)0.3170.349

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 head0.022
GPT teacher head0.299
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2023
Admission routes3
Has abstractyes

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