Bibliographic record
Abstract
Street life is a challenge for survival, even for adults, and is yet more difficult for children. They live within the city but are unable to take advantage of the comforts of urban life. This study focused primarily on access to health and education in street children from 6 to 18 years old in the Indian metropolises of Mumbai and Kolkata. The study also aimed to assess the role of social work interventions in ensuring the rights of street children. A combination of quantitative and qualitative research methodologies was used. Convenience sampling was used to recruit 100 children. Data were collected on a one-to-one basis through semi-structured interview schedules and by non-participant observation. Findings revealed that extreme poverty was the primary cause for the increasing numbers of street children. Lack of awareness among illiterate parents regarding educational opportunities kept most children away from school attendance. Factors such as lack of an educational ambience at home made it difficult for the children to work on their lessons outside the premises of the institution. It was evident that those living with their parents had better access to health care facilities than did those living on their own; however, nongovernmental organizations made significant efforts to redress this imbalance, setting up health check-up camps at regular intervals. Although exposure to harsh reality at an early age had resulted in a premature loss of innocence in most, making them sometimes difficult to work with, the nongovernmental organizations were striving to ensure child participation and the growth of individual identity. The interventionists therefore focused on developing a rights-based approach, rather than a charitable one.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".