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Record W2085535559 · doi:10.1080/00045600903245730

Oil Prices, Scarcity, and Geographies of War

2009· article· en· W2085535559 on OpenAlexaff
Philippe Le Billon, Alejandro Cervantes

Bibliographic record

VenueAnnals of the Association of American Geographers · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScarcityGeopoliticsContext (archaeology)NarrativePoliticsPolitical ecologyPeak oilPolitical economyEconomicsPolitical scienceDevelopment economicsEconomic geographyEconomyGeographyEcologyMarket economyLawClimate changeArchaeology

Abstract

fetched live from OpenAlex

Many commentators warn that oil scarcity increases the likelihood of war; we question this simplistic concept of scarcity-driven wars. Questioning the relationship between violence, scarcity, and oil begins from reconsidering the causal relationship between high prices and war: Wars can arise in the context of low prices, and the oil-related dimensions of conflicts that occur in the context of high oil prices cannot be solely reduced to struggles over dwindling resources. Based on a succinct review of recent studies, a discussion of major hypotheses, and a brief case study of Sudan, we suggest that scarcity is in part a narrative constructed for and through prices. Power relations resulting in massive financial windfalls mediate this narrative and its selective geographies of war and peace. We outline several hypotheses, and—drawing on critical geopolitics and political ecology—explore avenues for further studies incorporating spatially disaggregated analyses.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0030.007
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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 designObservational
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

Citations52
Published2009
Admission routes1
Has abstractyes

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