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Record W4400047124 · doi:10.31014/aior.1991.07.02.497

Social Harms of Child Labor in Afghanistan (Case Study of Bamyan City)

2024· article· en· W4400047124 on OpenAlexfundno aff
Ramazan Ahmadi, Mohammad Reza Akbari

Bibliographic record

VenueJournal of Social and Political Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
FundersInternational Development Research CentreAga Khan Foundation
KeywordsSociologyDemographic economicsPolitical sciencePsychologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.395
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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