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Record W2739283012

Collective Action on Climate Change: The Logic of Regime Failure

2007· article· en· W2739283012 on OpenAlexaboutno aff
Paul G. Harris

Bibliographic record

VenueDigital Commons - Lingnan (Lingnan University) · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsCollective actionClimate changeAction (physics)Environmental sciencePolitical scienceBusinessLaw and economicsEconomicsGeologyLawPhysicsOceanography
DOInot available

Abstract

fetched live from OpenAlex

The international climate regime, primarily designed to limit the emissions of pollutants causing global warming, has failed.Why has international cooperation to combat global warming been so difficult, and what factors must change to improve the situationassuming it is even possible?Using Mancur Olson's classical theory of collective action, this article endeavors to explain the failure of the climate regime.Other international environmental agreements and the associated regimes, such as the Mediterranean Action Plan and the Montreal Protocol on ozone depletion, demonstrate that collective action to address international environmental problems is possible.Both agreements contain the ingredients that classical theory suggests are necessary to achieve collective action.But the flipside of collective action theory-that collective action in larger groups is very difficult or unlikelycan also apply to international agreements and action on climate change.Despite the Mediterranean and Montreal successes, relatively speaking, and in spite of so much effort over two decades to create an effective climate regime, it is by no means apparent that the elements for success will exist for the foreseeable future.We should expect a continued muddling along that may, at best, reduce slightly-but not reverse-global warming at some point in the relatively distant future.Climate change is with us to stay.It is now patently clear that the world is facing a growing set of environmental dangers.The greatest among them is probably climate change -changes to Earth's climate system, manifested in events such as drought, floods, sea-level rise, major temperature rises in some regions (e.g., the Artic) and potentially precipitous falls in others (e.g., Europe), extinction of species, and spread of pests (to give but a sampling of the myriad *

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)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.034
Scholarly communication0.0060.008
Open science0.0010.006
Research integrity0.0040.006
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 head0.146
GPT teacher head0.250
Teacher spread0.105 · 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 designTheoretical or conceptual
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

Citations49
Published2007
Admission routes1
Has abstractyes

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