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Community-Based Organizations and Crime Prevention

2015· book-chapter· en· W2118755367 on OpenAlexaboutno aff
Tim Goddard, Andrea M. Headley

Bibliographic record

VenueOxford University Press eBooks · 2015
Typebook-chapter
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPublic relationsPolitical scienceContext (archaeology)CriminologyJuvenile delinquencyFidelitySociologyMedicineNursingEngineering

Abstract

fetched live from OpenAlex

Abstract Community-based organizations have proliferated throughout Australia, Canada, the United Kingdom, and the United States. Undergirded by the neoliberal privatization of turning social policy over to the market to foster “better” and cheaper social interventions, community-based organizations are funded to prevent adolescent “problem” behaviors including substance abuse, teenage pregnancy, school dropout, delinquency, and youth violence. This article reviews research on the practices and effectiveness of community-based organizations, mostly in the United States, regarding crime prevention. After discussing the background social context, the article reviews research on the range of services and programs that community-based organizations deliver followed by a review of the research on their effectiveness for preventing crime. The article then discusses a pattern by which organizations veer from program fidelity and reformulate and revise mandated evidence-based practices. It concludes with a discussion of some of the implications and possible consequences of shifting the provision of services to nonstate actors.

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)0.001
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: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.152
GPT teacher head0.359
Teacher spread0.208 · 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
Published2015
Admission routes1
Has abstractyes

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