MétaCan
Menu
Back to cohort
Record W4386441611 · doi:10.1163/15718069-bja10093

Climate Paradiplomacy: A Comparative Study of Canadian Provinces (British Columbia, Ontario, and New Brunswick)

2023· article· en· W4386441611 on OpenAlexaboutno aff
Annie Chaloux, Jennyfer Boudreau, Gabriel Grégoire-Mailhot, Philippe Simard

Bibliographic record

VenueInternational Negotiation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutionalisationClimate changeInternational relationsPolitical scienceState (computer science)SustainabilityRegional scienceGovernment (linguistics)PoliticsNegotiationGeographyEnvironmental resource managementPublic administrationLawEcologyEconomics

Abstract

fetched live from OpenAlex

Abstract Climate change is one of the most important issues of our time. However, its diffuse and complex nature makes it difficult to find global and effective solutions, as these require multi-scale contributions. Although the international climate regime primarily involves states, actors from other levels of government also have active international climate agendas. This is certainly true of several Canadian provinces. The objective of this study is to provide a comprehensive portrait of the paradiplomatic actions that Canadian provinces have undertaken around climate change. The comparative analysis presented here is based on a conceptual framework that distinguishes three types of paradiplomatic instruments: institutionalization, the use of intra-state routes, and the use of extra-state routes. Our analysis shows that Canadian provinces pursue similar paradiplomatic strategies, but that their intensity and sustainability vary widely according to the availability of economic and human resources, as well as political will.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.126
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.016
Science and technology studies0.0150.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.048
GPT teacher head0.344
Teacher spread0.296 · 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 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational NegotiationSame topicCross-Border Cooperation and IntegrationFrench-language works237,207