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Record W2964967646 · doi:10.3968/11061

The Effect of American’s Smart Power Approach and Nigeria Economic Recession, Issues and Prospects

2019· article· en· W2964967646 on OpenAlexvenueno aff
Maminat. S. Tenuche, Daniel Michael

Bibliographic record

VenueCanadian social science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Systems and Global Transformations
Canadian institutionsnot available
Fundersnot available
KeywordsRecessionEconomic powerEconomicsUnemploymentDominance (genetics)PoliticsCommodityPower (physics)Diversification (marketing strategy)Government (linguistics)Development economicsEconomic growthPolitical scienceBusinessMarket economyMacroeconomicsLaw

Abstract

fetched live from OpenAlex

America smart power approach and Nigeria economic recession discourses the effect of smart power on Nigeria economic recession. Nigeria and America had enjoyed all manner of relationship ranges from commercial, political and cultural values. This relationship between them reached a certain climax in 2010, but later decrease in the last four years. The decline in trade relations were because Nigeria refuse signatory to gay marriage proposed by America government. Thus, America adopted smart power approach to compel Nigeria signatory to gay marriage, proposal. The paper examines the effect of smart power on Nigeria economic recession. Data for this paper were drawn from secondary source mainly from the library and Nigeria dailies. Historical analysis were also analyze via content analysis. The paper finds that Nigeria over dependant of single commodity, increases weight of economic recession that create inflation, unemployment, high cost of living and reduction in taxation. Thus, the paper recommends that Nigeria government should imbibe on diversification of economic policy from producer of raw material to producer of finished goods to attract various trade relations; this is set out to weaken the America economic dominance in international politics.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.005
GPT teacher head0.263
Teacher spread0.258 · 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.

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
Published2019
Admission routes1
Has abstractyes

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