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Record W3018416602 · doi:10.1111/opec.12168

The Presidential Amnesty Programme of 2009 and Nigerian Oil Production: a disaggregate econometric analysis

2020· article· en· W3018416602 on OpenAlexaff
W. David Walls, Adegboyega Daniel During

Bibliographic record

VenueOPEC Energy Review · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAmnestyPresidential systemRevenueEconomicsProduction (economics)SurrenderPolitical scienceFinancePoliticsLawMacroeconomics

Abstract

fetched live from OpenAlex

Abstract We analyse the impact of the Presidential Amnesty Programme on crude oil production in Nigeria. The President of Nigeria instituted an amnesty programme in June 2009 to end the disruptive protests in the oil‐producing Niger Delta. Between 2006 and 2009, it is estimated that crude oil production losses exceeded 650,000 barrels per day, dramatically reducing government revenue. The amnesty programme provided militants a state pardon, educational training and a monthly stipend in exchange for the surrender of weapons. In this research, we use disaggregate oil‐well‐level data to estimate a difference‐in‐difference model of Nigerian crude oil production. The estimates reveal that the Presidential Amnesty Programme increased the oil output in the Niger Delta by about 40 per cent above the level that would have been achieved in the absence of the policy.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.204
Teacher spread0.174 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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