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Record W4200260434 · doi:10.33002/jelp01.03.02

Production Sharing Agreements: Learning Lessons from Russia and Nigeria

2021· article· en· W4200260434 on OpenAlexfundvenueno aff
Mary Sabina Peters

Bibliographic record

VenueJournal of Environmental Law & Policy · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
FundersEuropean CommissionGovernment of CanadaHarvard Business School
KeywordsInefficiencyProduction (economics)State (computer science)Foundation (evidence)BusinessEconomic systemInternational tradeEconomicsPolitical scienceMarket economyLawComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

This paper lays the foundation of what Production Sharing Agreements are, what they were intended to be, and how they have failed to meet the current requirements of the State and, in turn, have ended up exploiting the economic resources of the countries by not giving the State their rightful due. Moreover, this paper highlights the consequences of implementing the Production Sharing Agreements in two major oil producing States namely Nigeria and Russia. Subsequently, an earnest attempt has been made to bring to light the flaws of the Production Sharing Agreements accompanied with the inefficiency of the States to amend their respective laws according to their economic requirements.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.224
Teacher spread0.205 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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