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Schools and the Pathway to Crime

2018· reference-entry· en· W2947684707 on OpenAlex
Debra Pepler

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsYork University
Fundersnot available
KeywordsJuvenile delinquencyDropout (neural networks)School dropoutSchool violenceCriminologyCareer PathwaysSociologyPsychologyPublic relationsPolitical scienceSocial psychologyEngineeringComputer scienceOperations managementSocioeconomics

Abstract

fetched live from OpenAlex

This chapter focuses on the role of schools in the pathway to crime. It highlights research that points to the importance of relationships in development, reviews theoretical perspectives of relationships and the development of delinquency, and considers school violence. Next, the chapter focuses on the importance of school connections and relationships, with a consideration of engagement and bonding with school, failing academic performance, and dropout rates. School and classroom organization as well as relationships with teachers and with peers are identified as potential risk factors that further alienate troubled youth. Conversely, if these systemic and relationship processes are positive, they can mitigate the risk of moving along the pathway to crime. To conclude, the chapter revisits the theory and research, with a call to identify and intervene with the most vulnerable youth and leading to a discussion of implications for programming and policy.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.315
Teacher spread0.275 · 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

Quick stats

Citations21
Published2018
Admission routes1
Has abstractyes

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