Evidence ecosystem and development policies: Francophone West Africa Regional Profile
Bibliographic record
Abstract
Strengthening the use of evidence for more relevant and effective public policies in Francophone West Africa: The role of a Center of Excellence for better decision making The need to establish evidence-based public policies has become essential in West Africa, in the context of today's development challenges. High-quality data based on sound theoretical benchmarks enables the formulation of policies that are more effective and better suited to the needs of populations. The Evidence-Policy-Action Center of Excellence aims to facilitate the use of evidence for relevant strategic decisions in Francophone West African countries, thereby strengthening the legitimacy and positive impact of public interventions. Systems for producing and using data for public policymaking have been shaped by the region's colonial and post-colonial history, with significant disparities among countries and a strong influence from international institutions. To date, these systems have reproduced a tradition of public policies that rarely involve the beneficiary populations in an optimal way, thus reducing their influence on the choice of priorities and implementation methods. Today, West Africa faces many challenges (climate change, social inequalities, a demographic dynamic with a high proportion of low-skilled, unemployed young people, etc.) that require coordinated, evidence-based policy responses. The current evidence ecosystem in West Africa is characterized by a multiplicity of stakeholders (ministries, universities, NGOs, Think Tanks) and a fairly abundant output in the health, education, and agricultural sectors. However, disparities still exist within and between countries in terms of infrastructure and research services. Lack of coordination between advocates hinders synergy of efforts and limits the system's ability to produce high-quality data to support coherent and efficient policies. Moreover, the use of evidence is not well established, and there are no reliable platforms for sharing and leveraging data. In response to these challenges, the Evidence-Policy-Action Center of Excellence is acting as a catalyst to promote a strong evidence ecosystem in the region. It contributes to building the capacity of advocates and facilitating exchanges between researchers, decision makers, and practitioners. This center could offer greater harmonization and coherence of public policies through ongoing support for the use of reliable data for strategic and operational decisions. Strengthening West Africa's evidence ecosystem requires coordinated efforts to address structural barriers. Recommendations include strengthening regional cooperation, harmonizing data collection and analysis tools, and raising awareness about the centrality of evidence in public policy. Training and technical support programs for local stakeholders are essential to ensure the effective use and ownership of evidence in the region's sustainable development processes.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".