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Record W4367053074 · doi:10.48175/ijarsct-9462

Study on Juvenile Delinquency among Adolescents in Secondary Schools

2023· article· en· W4367053074 on OpenAlexaboutno aff
Bindi Patel, Mr. Parashram, Ishwar Das Vairagi

Bibliographic record

VenueInternational Journal of Advanced Research in Science Communication and Technology · 2023
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsJuvenile delinquencyNewspaperPsychologyCriminologyDevelopmental psychologyJuvenileTruancyHabitSocial psychologySociologyMedia studies

Abstract

fetched live from OpenAlex

Today, more than ever, the problem of juvenile delinquency pricks at the conscience of many societies, and India is not an exception. In this study, the researcher discusses the experiences and views of teachers of secondary schools regarding juvenile delinquency and the apparent moral decline among their learners. The problem was investigated from the perspective of socio-education, a field of research which, amongst others, studies the role of social factors that feature in the development of children and youth during their growth to adulthood. In Indian schools, the problem of juvenile delinquency is a issue. In his article on discipline in the Free State township schools, Masitsa (2008: 234- 236), cites a number of newspaper reports of juvenile delinquency among school children. This problem is not unique to South Africa, but affects many industrialised countries today (Rossouw, 2003: 416). De Wet’s study (2004: 206) on school vandalism, which is an example of antisocial behaviour, reveals that the problem also affects Britain, USA, Canada, France, the Netherlands and Australia, amongst others. MATERIALS AND METHODS: The research design for this study was a combination of quantitative and mainly qualitative, descriptive and interpretive enquiry, thus a mixed method research (MMR) approach. The study conducted on 91 samples. Data was collected using questionnaire and focus group interview. RESULTS: The delinquency rates in selected schools are very high. In addition, it was found that male learners, more than female learners, exhibit the most antisocial behaviour. In general, both genders, at varying rates disrespect authority, have the habit of lying, engage in theft, vandalism, truancy, substance and alcohol abuse, inappropriate sexual behaviour resulting in teenage pregnancy, and gangsterism, amongst others. The variable of location indicates that schools in formerly disadvantaged areas such as townships, squatter and rural areas experience the most antisocial behaviours by learners, as compared to their counterparts in formerly advantaged areas. Most teachers’ personal encounters of the problem in city schools seemed to be minimal. Some teachers of schools that are situated in middle class areas in townships shared similar sentiments to those of their city counterparts. Although the rate of learner misconduct is alarming, it can be contained as evidenced by behavioural changes witnessed in those learners who are remorseful. This suggests that intervention strategies do play a positive role in curbing unacceptable behaviour. Conclusion: The findings of this study suggested that the majority of teachers in secondary schools are aware of the huge challenge posed by delinquent learners with regard to teaching and learning. The major perceptions the study gathered were that the dawn of a democratic dispensation in 1994 worsened adolescent delinquency and rendered many previously disadvantaged schools dysfunctional

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.078
GPT teacher head0.469
Teacher spread0.390 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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