MétaCan
Menu
Back to cohort
Record W2751954887 · doi:10.5539/jpl.v10n4p225

Juvenile Crime: Current State and Dynamics

2017· article· en· W2751954887 on OpenAlexvenueno aff
Nikoli V. Valuiskov, Lubov V. Bondarenk, Ani D. Arutiunian

Bibliographic record

VenueJournal of Politics and Law · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicImpulse Buying and Technology Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsJuvenile delinquencyCrime preventionCriminologyAction (physics)Reliability (semiconductor)JuvenilePreventive actionState (computer science)Order (exchange)PsychologyScale (ratio)Computer scienceComputer securityEconomicsGeographyPower (physics)

Abstract

fetched live from OpenAlex

The article presents a comprehensive analysis of the problems of general and individual juvenile delinquency prevention. The definition of «general social crime preventive action» is given, its objectives and types are defined. The necessity of investing efforts and resources not in the repressive programs, but in the fundamental long-term programs aimed at the gradual elimination of social and economic disparities being the cause of the increase in crime rate among teenagers. The components and targets of individual crime prevention have been identified. The individual subjects of the juvenile crime prevention have been classified. The requirements for the subjects of the individual criminal behavior prediction have been formulated in order to create the theoretical and organizational prerequisites for the reliability of the individual behavior forecasts. As a result, the special measures of juvenile delinquency prevention have been proposed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.033
GPT teacher head0.276
Teacher spread0.242 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Politics and LawSame topicImpulse Buying and Technology ImpactsFrench-language works237,207