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Record W7096142269

Moral Super-Power or Policy Laggard? Translating Kyoto Protocol Ratification into Federal and Provincial Climate Policy in Canada

2005· article· en· W7096142269 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsKyoto ProtocolRatificationPer capitaGreenhouse gasGovernment (linguistics)NegotiationCorporate governanceClimate change
DOInot available

Abstract

fetched live from OpenAlex

Canada’s role in greenhouse gas reduction and climate change policy development has received far less scholarly attention than the roles of either the United States or the European Union. However, Canadian emissions are significant, comparable to the annual levels of the United Kingdom and ranking eighth among the nations of the world. These emissions, when measured on a per capita basis, are much closer to the higher levels of the United States and Australia than those of most EU nations. In turn, Canadian emissions have grown since 1990 at a rate substantially greater than that of the EU and somewhat greater than that of the United States. Stabilization and reduction of these emission levels thus poses an extraordinary challenge for Canada’s multi-level governance system. To date, the federal government has taken the lead in the negotiation of international agreements and pledges of Canadian commitments, consistent with its powers to make treaties. However, given the very limited role of the Canadian federal government in environmental and energy policy governance in Canada, much of the responsibility for policy development and implementation to honor these agreements is likely to be concentrated in provincial hands. Consequently, this case offers an intriguing

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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.232
Threshold uncertainty score0.890

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0200.012
Scholarly communication0.0150.004
Open science0.0020.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.000

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 head0.020
GPT teacher head0.331
Teacher spread0.311 · 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 designQualitative
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
Published2005
Admission routes1
Has abstractyes

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Same topicPolitical Systems and GovernanceFrench-language works237,207