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Record W2336529691 · doi:10.1111/ecin.12344

INTEGRATING MARKET ALTERNATIVES INTO THE ECONOMIC THEORY OF OPTIMAL DETERRENCE

2016· article· en· W2336529691 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.

Bibliographic record

VenueEconomic Inquiry · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSanctionsEconomicsDeterrence (psychology)Set (abstract data type)Function (biology)MicroeconomicsDeterrence theoryMaximizationFace (sociological concept)Law and economicsPolitical scienceComputer scienceLawSociology

Abstract

fetched live from OpenAlex

Leading economic models of crime assume that potential criminals achieve their ends by criminal means or not at all. We develop a framework in which potential criminals can also attain their objectives through voluntary trade. Our framework helps explain several features of the legal system that have proven to be problematic for the canonical approach: why optimal sanctions should be increasing in an individual's criminal history, and why necessity may be a partial defense in some situations. Finally, the inclusion of a voluntary trade option makes the maximization of a utilitarian welfare function identical to minimizing the costs of crime, implying that a long‐standing controversy in the literature is, in part, an artifact of the assumption that criminals face a binary choice set. ( JEL K42, D60, H00)

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.037
GPT teacher head0.247
Teacher spread0.210 · 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