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Record W3136304017 · doi:10.1093/bjc/azab003

Investing in crime prevention after the crisis: Social impact bonds, the value of (re) offending and the new ‘culture of crime control’

2021· article· en· W3136304017 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe British Journal of Criminology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsYork University
Fundersnot available
KeywordsPrisonInvestment (military)Crime controlBondCriminal justiceCriminologyFinancial crisisControl (management)Value (mathematics)Social controlPolitical scienceEconomic growthBusinessFinanceSociologyEconomicsManagementLawPolitics

Abstract

fetched live from OpenAlex

Abstract A recurring theme of criminal justice reform in the years following the financial crisis of 2008 has been the costs of incarceration and the effort to reduce correctional populations. This paper examines one aspect of this post-crisis landscape: the social impact bond (SIB). First piloted in Peterborough prison in 2010, SIBs use private investment to fund social programs with governments paying a return if these programs are successful. Drawing from research on SIBs in Canada, the United States and the United Kingdom, the paper explores this effort to turn (re)offending into an investment, its challenges and how SIBs reveal a financial ‘style of reasoning’ that is re-shaping the ‘culture of crime control’ with critical implications for providers, programs and participants.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.067
GPT teacher head0.292
Teacher spread0.225 · 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