Visibility of Nutrition Research and Dissemination Challenges in French Speaking Sub-Saharan Africa: A Bibliometric Analysis
Bibliographic record
Abstract
Although sub-Saharan Africa (SSA) is one of the regions in the world that is most affected by malnutrition and hunger, food science and nutrition related-publications by in-country-based authors from this region are rare. The objective of this paper is to analyze scientific production in French speaking SSA countries in the area of nutrition and food sciences (NFS). A bibliometric analysis was performed using the ISI Web of Knowledge database. We explored data for quantity and quality of publications between 1990 and 2009. Among the 21 sub-Saharan African countries with French as the official language, only 11 had more than 20 publications on NFS. This represents 4.7% of the total publications of these countries. The vast majority of the publications were in English (76.7%) despite the official and primary academic language is French in all eleven countries. The average number of citations of articles in French was 0.9 per paper compared to 6.7 per paper published in English. France was the main collaborating country with France-based researchers co-authoring 31.8% of the papers. Collaboration with other African countries was low and usually limited to neighbouring countries. In absolute numbers, Cameroon, Cote d’Ivoire and Benin were the most productive countries. When adjusted for population size, Mauritius, Gabon and Cameroon were the most productive countries per capita and when adjusted for average GDP, Cameroon, Burkina-Faso and Benin were the most productive. French speaking countries in Africa had a very low publication record in NFS and papers published in French were barely cited as compared to those published in English.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.099 | 0.144 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".