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Record W1588375599

Cambio climático y la convergencia transregional de los mercados de carbón en América del Norte

2010· article· es· W1588375599 on OpenAlexaboutno aff
Marcela López‐Vallejo Olvera

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2010
Typearticle
Languagees
FieldSocial Sciences
TopicRegional Development and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationClimate changeConvergence (economics)Corporate governanceWelfare economicsGreenhouse gasPolitical scienceGeographyBusinessEconomyEconomic growthEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

National policies in North America have not been drafted properly to address the problem of climate change, following the impasse of international negotiations. Facing this scenario, new alternatives emerge with the leadership and participation of new actors. Local governments in North America, especially of British Columbia, Ontario and Quebec, have been developing strategies to face climate change and emissions reduction in parallel to the national efforts and the global governance strategies. These local governments have developed a transregional approach that has resulted in the creation of regional institutions such as the Western Climate Initiative, the Regional Greenhouse GasInitiative and the Midwestern GreenhouseGas Reduction Accord. Their main goal is to establish regional carbon markets to mitigate and adapt to climate change impacts in a cost-effective way. In spite of these efforts, these initiatives have faced the overlapping problem among them and with national and globalstrategies. The goal of this research is to explore how these carbon markets have developed convergence policies. Convergence among these markets is expressed in their offset system and in secondary markets.

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)0.002
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: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.274
Teacher spread0.265 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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