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Record W4284899800 · doi:10.1111/1467-8268.12647

Intégration – commerciale, budgétaire, financière – régionale et inégalités de revenu dans la Communauté Economique des Etats de l'Afrique de l'Ouest (CEDEAO)

2022· article· fr· W4284899800 on OpenAlexaff
Léleng Kebalo, Hamitande Dout, Mawuli Kodjovi Couchoro, Stéphane Zouri

Bibliographic record

VenueAfrican Development Review · 2022
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPolitical scienceEconomicsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Résumé Cet article vise à mettre en lumière l'effet de l'intégration économique régionale sur les inégalités de revenu dans la Communauté Economique des Etats de l'Afrique de l'Ouest (CEDEAO). En se fondant sur la méthode des moments généralisés (MMG) en panel dynamique, nous analysons les effets de trois composantes différentes de l'intégration économique régionale – intégration commerciale, budgétaire et financière – sur les inégalités de revenu dans la CEDEAO. L'étude couvre la période 1990–2018. Notre analyse empirique montre (i) une persistance des inégalités de revenu, (ii) une absence totale d'effet de l'intégration financière régionale sur les inégalités de revenu et (iii) des effets réducteurs de l'intégration commerciale et budgétaire sur les inégalités de revenu dans la CEDEAO qui varient selon l'appartenance ou non d'un pays à une union monétaire et selon leur niveau de revenu. L'article fait des propositions de politiques économiques dont l'application permettrait de réduire ces inégalités dans la CEDEAO.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.033
GPT teacher head0.267
Teacher spread0.235 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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