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Record W2738199813 · doi:10.1057/978-1-137-58714-5_7

Boomtown Justice Systems

2017· book-chapter· en· W2738199813 on OpenAlexaff
Rick Ruddell

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBoomCriminal justiceWork (physics)Economic JusticePrivate securityPublic securityCriminologyPolitical scienceBaby boomBusinessPublic relationsPublic administrationEngineeringSociologyLawMedicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Increased occurrences of antisocial behavior, dangerous driving, and crime after a boom place significant demands on the police, courts, and corrections. This chapter describes the impact of the boom on these agencies, and respondents in boomtown studies report an increased number of calls for services from the public, delays in the time it takes cases to work their way through the courts, probation officers who find it difficult to manage their large caseloads, and overcrowded local jails. Turnover in many of these agencies is high as local agencies find it difficult to compete with the higher pay offered by the oil-field industries. In addition to describing the challenges faced by these public agencies, the roles that private security and first-responders play in crime prevention and responding to disorder and unintentional injuries are also profiled. Altogether, these public, private, and voluntary organizations play an important role in managing the disorder created by the boom.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.132
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0850.009

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 head0.065
GPT teacher head0.332
Teacher spread0.267 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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