PREPARATION FOR SOCIAL REINTEGRATION AMONG YOUNG GIRLS IN RESIDENTIAL CARE IN INDIA
Bibliographic record
Abstract
This study focuses on the preparation for social reintegration of young Indian girls about to leave their residential care homes. It assesses the level of preparation by capturing the perception of readiness of 100 girls in institutions: whether they expect to complete higher education, and whether they believe they have acquired such skills as searching for a job, managing finances, problem solving, and maintaining satisfactory relationships. It also explores the impact of different factors, such as the present age of the girls, their self-esteem, and the availability of support networks, on the preparation for their social reintegration. Overall, the findings revealed that the girls felt better prepared with life skills and access to housing after leaving care, but were not so hopeful about their psychological well-being and ability to access higher education, social support, employment, and financial independence. Factors such as age, educational qualifications, self-esteem, and availability of support while in care had a positive relationship with their preparation for social reintegration. Interestingly, the girls’ level of preparation varied significantly across the eight residential care homes studied. The study is intended to help address gaps in the existing literature and to play a significant role in informing future legislative decisions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".