Bibliographic record
Abstract
Revue semestrielle du Conseil pour le développement de la recherche en sciences sociales en Afrique et le Conseil africain pour l'enseignement de la communication CODESRIA would like to express its gratitude to the Swedish International Development Cooperation Agency (SIDA), the International Development Research Centre (IDRC), the Ford Foundation, the Carnegie Corporation of New York (CCNY), the Norwegian Agency for Development Cooperation (NORAD), the Danish Agency for International Development (DANIDA), the Netherlands Ministry of Foreign Affairs, the Rockefeller Foundation, the Open Society Foundations (OSFs), TrustAfrica, UNESCO, UN Women, the African Capacity Building Foundation (ACBF) and the Government of Senegal for supporting its research, training and publication programmes.Le CODESRIA exprime sa profonde gratitude à la Swedish International Development Corporation Agency (SIDA), au Centre de Recherches pour le Développement International (CRDI), à la Ford Foundation, à la Carnegie Corporation de New York (CCNY), à l'Agence norvégienne de développement et de coopération (NORAD), à l'Agence Danoise pour le Développement International (DANIDA), au Ministère des Affaires Etrangères des Pays-Bas, à la Fondation Rockefeller, à l'Open Society Foundations (OSFs), à TrustAfrica, à l'UNESCO, à l'ONU Femmes, à la Fondation pour le renforcement des capacités en Afrique (ACBF) ainsi qu'au Gouvernement du Sénégal pour le soutien apporté aux programmes de recherche, de formation et de publication du Conseil.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.540 | 0.356 |
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".