Does In-School Feeding Program Have Effect on Enrolment and Academic Performance? The Case of Public Primary Schools in Northern Ghana.
Bibliographic record
Abstract
\nThe study used a district-level data from 2008-2012 to examine the effect of school feeding program on enrolment and academic performance of pupils in food insecure district of Garu-Tempane, Upper East Region. A quasi-experimental design was used to select 360 pupils from participating and non-participating public primary school pupils. Results show that the feeding program succeeded in increasing gross enrolment rate by almost a quarter (24%) in participating schools. However, the enrolment rate in non-participating schools plummeted to as low as 7%.In terms of academic performance, participating pupils performed better in core subjects of English Language, Mathematics and Integrated Science than non-participating pupils. Furthermore, chi-square analysis of relationship between socioeconomic variables and academic performance showed that sex and occupation of parents/guardians were significant. However, chi-square analysis of relationship between socioeconomic variables and enrolments showed no significant relationship. In conclusion the program has succeeded in targeting the poor and vulnerable in participating school pupils. The findings have implication for access to primary schooling particularly for girls. The study therefore recommended that the feeding program should be well-targeted not only on the basis of food insecurity but through a more rigorous in-depth socio-economic survey and vulnerability mapping with a view to scaling-up of the program.\n
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".