MétaCan
Menu
Back to cohort
Record W7074008160

Game theory: building up cooperation

2013· article· en· W7074008160 on OpenAlexaboutno aff

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
Fundersnot available
KeywordsConventionGlobal governanceClimate governanceKyoto ProtocolCorporate governanceConference of the parties
DOInot available

Abstract

fetched live from OpenAlex

Can we achieve the ambitious mitigation targets needed to avert dangerous global warming? Research now shows that local sanctioning institutions may succeed where global agreements fall short. In spite of some 18 Conferences of the Parties, global efforts to curb emissions have failed to achieve tangible results (Fig. 1). Although participation is broad — there are 192 parties to the Kyoto Protocol under the United Nations Framework Convention on Climate Change (UNFCCC) — only a handful of nations are actually bound to reduce emissions. Furthermore, the lack of a supranational sanctioning institution means that countries are effectively free to disregard their commitments or to withdraw from the agreement (as Canada did). Due to the inherent trade-off between the breadth of the treaty, in terms of number of acceding countries, and the depth of the emission-reduction commitments, game theorists have come up with the dismal prediction that little will be achieved by a self-enforcing agreement. Either the number of signatories will be small, or many countries will partake in a shallow agreement and achieve only modest reductions. In Nature Climate Change, Vasconcelos and colleagues provide reasons for optimism: local climate governance may be less riddled with barriers to cooperation than international agreements.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.008
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.335
Teacher spread0.308 · 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 teacher head, not a consensus.

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science)Same topicTheoretical and Computational PhysicsFrench-language works237,207