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Record W2310254337

Les fragilités de la microfinance au Cameroun (The Fragile State of the Microfinance Sector in Cameroon)

2012· article· fr· W2310254337 on OpenAlexaff
Nathanael Ojöng

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsTyndale University
Fundersnot available
KeywordsMicrofinancePolitical scienceContext (archaeology)Financial crisisHumanitiesWelfare economicsEconomyGeographyEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

French Abstract: Le secteur de la microfinance au Cameroun joue un role vital qui rend necessaire l’analyse de la crise dans le secteur. Cet article soutient que les causes de la crise sont complexes et profondes et va donc au-dela de la mauvaise gestion de certains etablissements de microfinance tels que la Cooperative financiere de l’estuaire SA. L’insuffisance de la reglementation prudentielle et de la supervision, la qualite du portefeuille et la mauvaise gestion sont des facteurs contributifs a la crise. Ces facteurs doivent etre compris dans le contexte du Cameroun, ce qui exige la prise en compte de ses specificites locales. Nous montrons que la crise de la microfinance dans le pays n’est pas liee a la crise financiere mondiale. Le secteur etait deja en mauvaise sante avant meme la recente crise financiere.English Abstract: Mindful of the strategic position of the microfinance sector in Cameroon, it is crucial to examine the microfinance crisis in the country. This paper argues that the causes of the crisis are complex and deep, and extend beyond the bad management of some microfinance institutions such as the Cooperative financiere de l’estuaire SA. Inadequate prudential regulation and supervision, poor portfolio quality and bad management are contributory factors to the crisis. These issues must be understood within the context of Cameroon, which means taking into consideration local specificities. We show that the microfinance crisis in the country is not linked to the recent global financial crisis, since the sector was already in bad health prior to the crisis.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.123
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.232
Teacher spread0.219 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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