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Record W1984670992 · doi:10.1177/1743872107086147

On the Book of Job, Justice, and The Precariousness of the Criminal Law

2008· article· en· W1984670992 on OpenAlexaff
Benjamin L. Berger

Bibliographic record

VenueLaw Culture and the Humanities · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsUniversity of VictoriaYork University
Fundersnot available
KeywordsScrutinyTheory of criminal justiceCriminal lawCriminal justiceLawInjusticePolitical scienceMythologyEconomic JusticeSubject (documents)SociologyCriminologyPhilosophy

Abstract

fetched live from OpenAlex

The criminal law has been subject to both increased demands in the societal functions that it is expected to perform, and heightened scrutiny for those points at which it fails to achieve these ends. The resulting pressures put into question the criminal law's capacity to perform justice. Rather than turning to contemporary sources to assess the criminal law's relationship to claims of justice, the author uses an analysis of the ancient myth found in the Book of Job as a means of exposing the irresolvable tensions at the core of the criminal law system's quest for justice. In the end, injustice manifests as senseless suffering. The profound precariousness of contemporary criminal law is that its prescribed task is to make sense of suffering but it is always unable to wholly achieve this goal and is, indeed, always on the precipice of making things worse.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.009
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.047
GPT teacher head0.270
Teacher spread0.223 · 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 designTheoretical or conceptual
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

Citations2
Published2008
Admission routes1
Has abstractyes

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