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Record W3159075066 · doi:10.31219/osf.io/x82yh

Crime and Punishment: Adam Smith’s Theory of Sentimental Law and Economics

2020· article· en· W3159075066 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

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsTrinity College
Fundersnot available
KeywordsResentmentPunishment (psychology)Rational choice theory (criminology)SanctionsAdam smithUnintended consequencesEconomic JusticeLaw and economicsPublic goodFeelingEconomicsCriminologyLawSociologyPolitical sciencePsychologySocial psychologyMicroeconomicsNeoclassical economics

Abstract

fetched live from OpenAlex

For Adam Smith a crime is not the result of a rational calculation of loss and gain, but the consequence of envy and a vain desire to parade wealth to attract the approbation of others, combined with a natural systematic bias in overestimating the probability of success. Similarly, Smith does not conceive of legal sanctions as a rational deterrent, but as deriving from the feeling of resentment. While the prevailing approach of the eighteenth century is a rational explanation of crime and a utilitarian use of punishment, Adam Smith instead builds his theory of criminal behavior and legal prosecution consistently on the sentiments. A well-functioning legal system is thus an unintended consequence of our desire to bring justice to the individual person, not the result of a rational calculation to promote the public good, just like a well-functioning economic system is the unintended consequence of our desire to better our own condition, not the result of a rational calculation to promote public good.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.273
Teacher spread0.242 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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