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Record W4315784007 · doi:10.21203/rs.3.rs-2459425/v1

Power in the Pipeline: Gas Centrality Reduces Leader Turnover

2023· preprint· en· W4315784007 on OpenAlexaff
Quentin Gallea, Massimo Morelli, Dominic Rohner

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsHealth Care Foundation
FundersUniversità BocconiUniversità degli Studi di PadovaUniversité de LausanneMonash University
KeywordsCentralityPipeline (software)Power (physics)BusinessPower to gasPetroleum engineeringComputer scienceChemistryEngineeringPhysicsMathematicsThermodynamicsOperating system

Abstract

fetched live from OpenAlex

Abstract This paper provides the first comprehensive empirical analysis of the role of natural gas for the domestic and international distribution of power. The crucial role of pipelines for the trade of natural gas determines a set of network effects that are absent for other natural resources. Gas rents are not limited to producers but also accrue to key players occupying central nodes in the gas network. Drawing on our new gas pipeline data, this paper shows that gas centrality of a country increases substantially the ruler's grip on power as measured by lower leader turnover. A main mechanism at work is the reluctance of connected gas trade partners to impose sanctions, meaning that bad behavior of gas-central leaders is tolerated for longer before being sanctioned. Overall, this reinforces the notion that fossil fuels are not just poison for the environment but also for political pluralism and healthy regime turnover.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.003

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 head0.181
GPT teacher head0.364
Teacher spread0.183 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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