MétaCan
Menu
Back to cohort

Beyond Kyoto: Climate Change Policy in Multilevel Governance Systems

2007· article· en· W2140518477 on OpenAlexaboutno aff
Barry G. Rabe

Bibliographic record

VenueGovernance · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceMulti-level governanceRealmClimate changePolitical scienceClimate policyKyoto ProtocolInternational relationsEnvironmental resource managementPublic administrationEconomic systemEconomicsPolitics

Abstract

fetched live from OpenAlex

Climate change policy has commonly been framed as a matter of international governance for which global policy strategies can be readily employed. The decade of experience following the 1997 signing of the Kyoto Protocol suggests a far more complex process involving a wide range of policy options and varied engagement by multiple levels of governance systems. The respective experiences of the United States and Canada suggest that formal engagement in the international realm of policy is not a good indicator of domestic policy development or emissions reductions. The different contexts of intergovernmental relations, varied resources available to subnational governments for policy development and implementation, and role of subnational leaders in policy formation have emerged as important factors in explaining national differences between these North American neighbors. Consequently, climate change increasingly presents itself as a challenge not only of international relations but also of multilevel governance, thereby creating considerable opportunity to learn from domestic policy experimentation.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0070.014
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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

Citations241
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueGovernanceSame topicPolicy Transfer and LearningFrench-language works237,207