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Record W3169290543 · doi:10.6000/1929-4409.2021.10.127

Environmental Factors and Hot Spot Areas of Juvenile Delinquency in Bangkok

2021· article· en· W3169290543 on OpenAlexvenueno aff
Tanet Ketsil, Mutita Markvichit, Wanwipa Thongsri

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
FundersNational Research Council of Thailand
KeywordsJuvenile delinquencyJuvenileCriminologyForensic engineeringPsychologyEnvironmental scienceEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

This research surveyed and mapped the juvenile delinquency risk areas in Bangkok, to establish environmental development measures to reduce the risk. This qualitative research was conducted in the Geographic Information System area using surveys of Bangkok Metropolitan Police Divisions 1–9, 36 in-depth interviews with non-commissioned and commissioned police officers and two official seminars. Finally, the research results were used to develop an online map in the Google Maps system. The analysis of the environmental problems of the juvenile delinquency areas’ survey map revealed four main juvenile crimes: 1) drugs, 2) property3) assaultand4) motorcycle drag racing; they also identified 30 risks areas most of which were slums, secluded alleys with inadequate lighting, entertainment venues, and convenience stores open 24 hours a day.

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)0.000
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 score0.055
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.363
Teacher spread0.285 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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