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Record W93002848 · doi:10.5206/cie-eci.v37i2.9117

Appropriations de la decentralisation et regulation des systemes educatifs en Afrique subsaharienne : Une analyse comparee des cas du Benin et du Senegal

2008· article· fr· W93002848 on OpenAlexaffvenue
Madeleine Tchimou

Bibliographic record

VenueComparative and International Education · 2008
Typearticle
Languagefr
FieldSocial Sciences
TopicPhilosophy, Sociology, Political Theory
Canadian institutionsUniversité de Montréal
FundersMinistère de l'Éducation Nationale
KeywordsCentralisationHumanitiesPolitical scienceDecentralizationContext (archaeology)PhilosophyGeography

Abstract

fetched live from OpenAlex

Cette recherche analyse la régulation des systèmes éducatifs du Bénin et du Sénégal dans le cadre de la décentralisation de l’éducation qui leur a été prescrite à travers les politiques d’ajustement structurel. Quatre aspects clés du système éducatif ont été analysés: l’offre d’éducation, les programmes d’enseignement, l’inspection et/ou le contrôle des enseignants et la condition enseignante. L’étude de cas à partir de données invoquées a été utilisée. Il ressort de l’analyse comparée des portraits nationaux que les appropriations nationales de la mise en application de la décentralisation demeurent assez distinctes dans ces deux pays et que la régulation actuelle de leurs systèmes éducatifs repose sur un modèle composite. This paper analyzes the regulation of education systems in Benin and in Senegal in the context of decentralization as prescribed through the structural adjustment policy. Four key elements of the education system are considered: access, curriculum, inspection and, or supervision of teachers, and teaching conditions. The author develops a case study from the available data. The comparative analysis of the national profiles demonstrates that applications of the decentralization process are quite different between these two countries and that the current regulation of the education systems is built on a composite model.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.563
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
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 head0.095
GPT teacher head0.420
Teacher spread0.325 · 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
Published2008
Admission routes2
Has abstractyes

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