Child labour, gender inequality and rural/urban disparities
Bibliographic record
Abstract
National poverty strategies frequently overlook important elements of children’s experiences of poverty, including trafficking, sexual exploitation and access to information about how to avoid HIV/AIDS. Non-immediate impacts of broader economic development policies on children’s well-being often remain largely invisible. This paper primarily investigates factors relating to child labour and child schooling in order to understand the possible impact of policies on households and children. The authors consider the extent to which the Ethiopian Sustainable Development and Poverty Reduction Programme (SDPRP) is making a difference to poor children’s lives, particularly in respect to agricultural policy and its impact on school enrolment and child work. The authors also focus on how change varies depending on gender and rural-urban differences. They assess whether the SDPRP’s focus on labour-intensive agricultural production pressures children to stay at home and carry out agricultural, domestic and care tasks while their parents work. The authors consider whether parental education levels influence decisions about children’s school attendance, and whether there is a gender difference in time spent on labour and on school enrolment rates. They also consider whether children in female-headed households have greater pressure to forego educational opportunities in order to work.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".