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Record W3176163032

Blending the Legal and Institutional Framework With the Economy of Communion as a New Paradigm for the Fight Against Corruption in Developing African Countries: The Case of Cameroon

2016· article· en· W3176163032 on OpenAlexvenueno aff
Abue Ako Scott Eke, Cyprain Mbou Monoji

Bibliographic record

VenueStudies in sociology of science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLanguage changeTransparency (behavior)PovertyColonialismDevelopment economicsCorrupt practicesPolitical sciencePolitical economyDeveloping countryStandard of livingEconomic growthEconomicsLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

Corruption in Africa is a colonial legacy. However, this cankerworm grew in strength in the early 1990s as a result of the economic crisis that affected the Cameroonian and most of African economies in the late 1980s, stretching to the mid 1990s. As a result of this economic crisis, the currencies of most African States were devaluated and the salaries of workers were slashed. Extreme poverty then crept into the lives of these workers as they could barely meet up with their daily needs. To ameliorate these poor living standards, most workers resolved to corrupt practices and so corruption grew in strength and in might. African countries were propelled to the top of corruption rankings by corruption watchdog Transparency International. From thence, African governments have been relentless in their efforts to fight against corruption. This paper therefore carries out a critical analysis of the laws and institutions put in place for the fight against corruption in Africa with the case study being Cameroon. It equally assesses the extent of their success and the reasons for their failure. Above all it proposes a new paradigm for the fight against corruption in the developing countries of Africa which is the Economy of Communion.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.983

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 studies0.0010.020
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 head0.035
GPT teacher head0.301
Teacher spread0.266 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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