An Empirical Analysis of the Child Labor in the Carpet Industry of Kashmir: Some Major Findings
Bibliographic record
Abstract
In the present paper an attempt has been made to study various facts of child labor. Children have been defined here as those in the age group of 6-14 years, working in their family owned or non-family carpet weaving units. The major focus was (I ) to find socio-educational life pattern of child labor, (ii) to locate factors compelling them to join labor force, (iii) to identify their socio-economic and family background, (iv) to delineate various positive and negative aspects of their working conditions,(v) to find the level of earnings of child and its impact on household income, (vi) to highlight the effects of abolition of child labor on the household, (vii) to identify the role of employers in eliminating child labor, and (viii) to know the opinion of parents and employers on child labor. Multi- stage sampling was used to select child labors from 100 households engaged in carpet units of five village of Quimoh development block of Kulgam district, which was the universe of the present study. In its effort to collect more authentic data, the study has included parents (82) of the child workers and their employers (50) as well. The tools used for data collection were interview schedules besides observations, block development offices etc. The major findings and conclusions that emerge from the analysis and discussion are briefly summarized in the present paper. Apart from these, certain important recommendations are made to ameliorate the condition of child workers in contemporary society and also to prevent the entry of children in carpet weaving in future society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".