Bibliographic record
Abstract
This report summarizes lessons learned and key policy findings on the World Bank's work in education in Senegal. In 1993, Senegal established a new policy for literacy programs based on partnership between civil society and the state: the state ensures policy leadership, overall coordination, monitoring and evaluation; the providers (civil society organizations, such as non-profits, village associations, and language associations) implement local literacy activities; an independent contract-managing agency handles contracts and rapid transfer of funds to providers. The World Bank financed project achieved the following results, which were similar to those achieved by the Canadian International Development Agency (CIDA): about 190,000 participants, of which 87 percent were women, enrolled in literacy classes over a five-year period; capacity in government and civil society organizations improved consistently; the dropout rate averaged 15 percent (much lower than for most adult literacy programs); most participants achieved learning mastery levels for reading (although not for math). The results of the literacy training exceeded target levels. Learning outcomes systematically improved as a result of two factors: providers became more experienced; and research led to improved procedures. The report notes that weak monitoring and evaluation contributed to the following shortcomings: re-financing of low-quality providers; lack of information about impact. As a result of these problems, some of the literacy courses did not provide adequate learning for the participants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".