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Record W4401922197 · doi:10.33458/uidergisi.1539238

Impact of the Montreal Protocol on Decision-Making Processes for Participation: A Case Study of Türkiye

2024· article· en· W4401922197 on OpenAlexaboutno aff
Sefa Öztürk

Bibliographic record

VenueUluslararası İlişkiler Dergisi · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Environmental Law and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal ProtocolConventionPolitical scienceContext (archaeology)IncentivePoliticsKyoto ProtocolProtocol (science)Regional scienceGeographyEconomicsLawOzone layerClimate change

Abstract

fetched live from OpenAlex

This study provides a comprehensive analysis of Türkiye's decision-making processes in the context of the international ozone regime, particularly focusing on the country's engagement in the Vienna Convention and the Montreal Protocol during the 1980s and early 1990s. Utilizing process tracing methodology and primary sources, and adopting an interest-based framework the research delves into Türkiye's involvement and pinpoints the principal determinants of its international environmental policy. The study argues that Türkiye's approach to environmental cooperation in the Convention and Protocol was shaped by the incentive and sanction provisions of the Montreal Protocol, efforts to align Türkiye's commercial and political relations with the European Community, the growing involvement of Western countries in the agreements, and the potential environmental prestige gained from cooperative endeavors. The study emphasizes the trade provisions and Article 5 status within the Montreal Protocol, both of which played a critical role in influencing Türkiye's policy choices. This significance primarily stems from the requirement for new calculations in abatement costs. It illuminates the causal links between specific design elements of the ozone regime and their impact on Türkiye's policy decisions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.791

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)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.362
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2024
Admission routes1
Has abstractyes

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