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Record W2807239117 · doi:10.3917/vse.204.0127

Répression des infractions commerciales liées aux prix et niveau des prix des produits alimentaires au Cameroun

2018· article· fr· W2807239117 on OpenAlexaff
Mathieu Juliot Mpabe Bodjongo, J. Landry Bikai, Jules Médard Nana Djomo

Bibliographic record

VenueVie & sciences de l entreprise · 2018
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsMinistère des Transports
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

L’objectif de cette étude est d’analyser l’impact de la répression des infractions commerciales sur le niveau des prix des produits alimentaires au Cameroun pendant la période 2005-2016. Pour atteindre cet objectif, un modèle théorique a été élaboré en s’inspirant de la théorie des anticipations adaptatives. En utilisant les données collectées auprès de l’Institut National de la Statistique (INS), de la Banque Mondiale et du Ministère du Commerce du Cameroun, il apparait que le nombre d’opérateurs économiques sanctionnés pour infractions commerciales liées aux prix croit, tandis que l’indice des prix à la consommation des produits alimentaires diminue. Les résultats économétriques obtenus à l’aide de l’estimation d’un modèle linéaire autorégressif d’ordre 1 révèlent que la répression des infractions de prix et la contraction de la quantité de la masse monétaire favorisent la réduction des prix sur le marché intérieur des produits alimentaires au Cameroun.

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)0.004
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 score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.092
GPT teacher head0.345
Teacher spread0.253 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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