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Record W3177694375 · doi:10.6000/1929-4409.2021.10.140

Social Structure as the Root of Improving Criminality in the Era of Pandemic Covid-19

2021· article· en· W3177694375 on OpenAlexvenueno aff
Nur Khalimatus Sa’diyah, Umi Enggarsasi

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Social Justice Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicGovernment (linguistics)Coronavirus disease 2019 (COVID-19)CriminologySociologyRoot causePolitical scienceEconomicsMedicine

Abstract

fetched live from OpenAlex

A level in society which is a social structure or patterns that foster community life that has a role in an organization in a group. In the face of the Covid-19 Pandemic, the Middle Class and Lower Class Communities were greatly affected by their social and economic conditions, this has become a problem for the Increase of Crime in the era of the Covid-19 Pandemic. The problems in this study are First, what are the factors causing the social structure as the root of the increase in crime in the Covid-19 pandemic era. Second, how to deal with the increase in crime in the era of the Covid-19 pandemic. The research method used is juridical empirical, using primary and secondary data, and qualitative analysis presented descriptively. The result of this research is that with the existence of a social structure as the cause of crime that occurred during the Pandemic, the government must immediately find a solution and overcome it so that the crime rate can suppress and decrease.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.095
GPT teacher head0.412
Teacher spread0.316 · 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 designTheoretical or conceptual
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

Citations4
Published2021
Admission routes1
Has abstractyes

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