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Record W2596693667 · doi:10.3138/jcfs.33.2.249

Competence and Family Support of Vulnerable and Invulnerable Adolescents Representing Scheduled Tribes and Scheduled Castes in India

2002· article· en· W2596693667 on OpenAlexvenueno aff
David K. Carson, Aparajita Chowdhury, Reeta Choudhury, Cecyle K. Carson

Bibliographic record

VenueJournal of Comparative Family Studies · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsCasteDisadvantagedSocioeconomic statusEthnic groupPsychologyExtended familyDevelopmental psychologyPopulationDemographySociologyEconomic growth

Abstract

fetched live from OpenAlex

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.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.077
GPT teacher head0.348
Teacher spread0.271 · 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 designObservational
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

Citations8
Published2002
Admission routes1
Has abstractyes

Explore more

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