Assessing the Impact of Public-Private Funded Midday Meal Programs on the Educational Attainment and Well-being of School Children in Uttar Pradesh, India
Bibliographic record
Abstract
Abstract The provision of meals at schools is considered to have the potential to enhance human dignity and facilitate equitable access to students from low socio-economic backgrounds, low social status (including Caste) and poor households. Using students and teachers from public schools in Utter Pradesh also known to be India’s most populous and poorest state as it’s as its unit of analysis, the paper examines the impact on International Non-Governmental Organizations (INGOs) led Midday Meal (MDM) School Feeding program in India on educational access, performance, participation, and wellbeing. The study sought to evaluate the implementation of the midday meal (MDM) program led by an INGO in Lucknow, Utter Pradesh, and India to ascertain if the strategic program implementation protocols also ensure social inclusion and held address various forms of discrimination commonly reported in the literature. The study revealed that students were satisfied with most of the implementation of the program, serving and food satisfaction indicators. Nevertheless, we argue that the implantation process could benefit from a more integrated inter-agency coordination so as to address concerns regarding at-risk children and improve sanitation and health facilities that are not directly associated with the MDM program. The study concludes that INGOs led MDM programs could serve as a model for inclusive and non-discriminatory school feeding system where all children, irrespective of their social, economic, religious and family backgrounds will equally benefit with dignity. Such an approach, we argue, could also enhance social equity, youth development and the attainment of the SDG targets in India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".