The National Governance Programme (2006—10) and the modernization of the administration: Cameroon and New Public Management
Bibliographic record
Abstract
New Public Management (NPM) emerged in the 1980s as the recognized instrument of public administration modernization. It has been introduced into African countries through National Governance Programmes (NGPs), programmes that set out to reform and improve the action of the State. It is only natural that we should study the impacts, whether concrete or planned, of their application. That is the core purpose of this document, which looks into the mutation of Cameroon's public administration from the perspective of NPM on the basis of the reform planned by the latest NGP straddling 2006—10. It also analyses the reasons that prompted Cameroon to adopt NPM, as well as its political and, in particular, administrative consequences. Points for practitioners The article examines the administrative reform planned by the National Governance Programme (2006—10) in Cameroon. Given that this reform can be interpreted as a consecration of New Public Management (NPM) as the principle behind the modernization of public administration in this country, it sets out to analyse the process involved. Moreover, the author also looks at the underlying causes of this reform and at its political and administrative implications. The article adopts a critical approach and raises the fundamental issue of institutional transfer from the countries of the North towards those of the South, and the consequences for African reform projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".