Competence and Family Support of Vulnerable and Invulnerable Adolescents Representing Scheduled Tribes and Scheduled Castes in India
Bibliographic record
Abstract
This study examined family strengths of disadvantaged adolescents that may serve as buffers against the adversities of everyday life in rural India. Two-hundred adolescents and their families belonging to Scheduled Tribe (ST) and Scheduled Caste (SC) groups in Orissa State were selected as participants. Within these families, 100 adolescents were identified as invulnerable (i.e., disadvantaged-competent), and another 100 as vulnerable (disadvantaged-incompetent) on the basis of peer and teacher nominations. Demographically, families of the vulnerable and invulnerable adolescents were similar with respect to their racial, ethnic, socioeconomic status, caste composition, and family structure (predominantly two parents, siblings, and other family members living together in one residence). However, there were a number of significant differences between the vulnerable and invulnerable adolescents with regard to their perceptions of their families and general living environment, as well as their own social and academic competencies. The findings shed light on a variety of risk factors inherent to the disadvantaged-incompetent group. The results also highlight the importance of particular family and community protective factors that may promote successful development in adolescents reared in extremely resource-limited rural families in India.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".