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Record W2207736820 · doi:10.5539/ilr.v5n1p1

Child Rights As Perceived by the Community Members in India

2015· article· en· W2207736820 on OpenAlexvenueno aff
Sibnath Deb, Jiandong Sun, Anjali Gireesan, Aneesh Kumar, Anindita Majumdar

Bibliographic record

VenueInternational Law Research · 2015
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
FundersDivision of Human Resource DevelopmentMinistry of Education, IndiaIndian Council of Social Science Research
KeywordsChild rightsPerceptionHuman rightsPolitical sciencePsychologyLawSocial psychologySocioeconomicsSociology

Abstract

fetched live from OpenAlex

Attitudes, knowledge, and perceptions of an individual influence their behavior as well as culture of a society. The objective of the study was to understand the attitudes and knowledge of 584 Indian community members regarding child rights and their perceptions about whether selected child rights were secured in reality. Overall attitudes of vast majority (96 – 98%) of the participants towards child rights were found to be positive i.e., children should have rights in various respects except issue like right to meet others (Article 15 of CRC). Knowledge of majority of the participants about child rights related legislations was moderate and varied across the cities while participants were unanimous about poor lived experiences of child rights in reality. So far as attitude and perception are concerned about child rights, there was a significant difference in the distribution between cities (p<0.01). Overall, the Rights of Children to Free and Compulsory Education Act, 2009 had the highest awareness (91.3%, n=533), followed by the Child Labour (Prohibition and Regulation) Act, 1986 (89.7%, n=523) and the Prohibition of Child Marriage Act, 2006 (89.6%, n=523). Findings of the present study speak in favor of community awareness about child rights and penalties for violation of child rights.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.111
GPT teacher head0.437
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2015
Admission routes1
Has abstractyes

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