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Record W4297061587 · doi:10.3390/jrfm15090415

Does FDI Promote the Resource Curse in Nigeria?

2022· article· en· W4297061587 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentGranger causalityEconomicsExchange rateCointegrationNatural resourceResource curseGross domestic productError correction modelCurseWelfareMonetary economicsInternational economicsMacroeconomicsMarket economyEconometrics

Abstract

fetched live from OpenAlex

This study investigated whether Foreign Direct Investment (FDI) supported the resource curse hypothesis in Nigeria. The precise methodological contribution was based on the Vector Error Correction and Granger causality test. The finding showed cointegration among the variables, whereas the speed of adjustment was slightly low. Similarly, natural resource to gross domestic product, FDI, and exchange rate unidirectionally Granger cause economic welfare, whereas bidirectional Granger causality is observed between indicators of natural resources to export, trade, and economic welfare. The results clearly indicate that FDI and natural resource management could improve economic wellbeing, although with a cost of volatility in the exchange rate and utilisation of resources. Thus, the study recommends the urgent need for effective and efficient management of the country’s natural resources to attract foreign direct investment and generate growth that can contribute meaningfully to the welfare of the citizens. Likewise, there is a need to diversify oil resources to other non-natural resources for the economy to stimulate growth and reduce the vulnerability of the economy to external shocks.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.007
GPT teacher head0.181
Teacher spread0.173 · 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