Social Harms of Child Labor in Afghanistan (Case Study of Bamyan City)
Bibliographic record
Abstract
The social harms of child labor in Bamyan City, Afghanistan, represent a fundamental and critical issue with widespread negative impacts on both children and society. Due to poverty and economic difficulties, children are compelled to undertake hard and exhausting work, depriving them of their basic rights such as education and play. The aim of this study is to identify the factors influencing child employment, evaluate its social and psychological consequences, and provide effective solutions to reduce and eliminate these harms. This quantitative research gathered information using questionnaires from child labor in Bamyan City and analyzed it with SPSS software. The results show that half of the children are deprived of education and mostly work in mechanics and blacksmithing with low income. Their guardians are predominantly unemployed or day laborers, and these children live in large families. The primary reasons for child employment include poverty, unemployment, the need for skill acquisition, deprivation from school, and the incapacity or illness of parents. Child labor leads to serious social harms such as depression, hopelessness, illness, aggression, encouragement towards drug use and crimes, and sexual abuse. Some children feel hopeless about the future, while others have aspirations such as becoming skilled workers, doctors, engineers, and politicians. This situation requires serious and urgent measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".