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Record W3200967906 · doi:10.33423/jabe.v23i1.4058

Exploring the Impact of Anti-Social Behaviour, Drug-Related Crime and County Lines on Local Communities: A Qualitative Inquiry

2021· article· en· W3200967906 on OpenAlexvenueno aff
Stuart Bavington

Bibliographic record

VenueJournal of Applied Business and Economics · 2021
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsResentmentHostilityQualitative researchCriminologyQualitative propertyLocal communityPerceptionSociologyQualitative analysisSocial psychologyPsychologyPublic relationsPolitical scienceSocial sciencePoliticsLaw

Abstract

fetched live from OpenAlex

This qualitative research project explores the impact of Drug-Related Crime, Anti-Social Behaviour, and County Lines on local communities. The study aimed to elicit perceptions, views and insight of this impact from local community members. Data for the inquiry was collected by conducting semi-structured qualitative interviews with key informants chosen from local communities. The findings show that Anti-Social Behaviour has had a significant impact on the local communities chosen for the inquiry. In contrast the findings were indicative that the impact of Drug -Related Crime to be much less significant. Although open drug dealing had been a cause for concern reports of acquisitive crime normally associated with “problematic drug use” (Gordon et al., 2007) were extremely low. Evidence in the findings suggest that the overall impact of County Lines within the communities has led to certain amount of resentment and even hostility from local gangs. Included in the conclusion are suggestions for future exploration and research based on the findings of this inquiry.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.239
GPT teacher head0.435
Teacher spread0.195 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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