Etat des systèmes de suivi et d’évaluation en Afrique francophone : une approximation au moyen d’un diagnostic rapide
Bibliographic record
Abstract
The state of monitoring and evaluation systems in Francophone Africa: An approximation through rapid diagnosis Background: Francophone African countries are facing strong internal and external pressures to promote monitoring and evaluation systems (M&E). Unfortunately, the state of development of M&E functions in these countries are poorly known. Objectives: This study aims to assess the status of selected components of M&E systems in Francophone Africa from an action-research perspective. Method: This study uses two different methods: a literature review and an online survey of key informants in 23 Francophone African countries. Results: The results highlight the current context of monitoring and evaluation frameworks, including the regulatory frameworks, policies and guides on M&E as well as the formal structures responsible for M&E in surveyed countries. Furthermore, results indicate a weakness in M&E capacity building frameworks. Conclusion: Poor knowledge of the state of M&E in Francophone African countries is an impediment to the process of institutionalising M&E and evidence-based decision making. Contribution: By highlighting the state of M&E institutionalisation, the results of this research will provide guidance to the various stakeholders on the measures to be put in place to effectively support Francophone African countries in their process of institutionalising M&E of public policies.
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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 | 0.196 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".