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Record W3005824885 · doi:10.3138/utlj.2019-0063

Civil order, markets, and the intelligibility of the criminal law

2020· article· en· W3005824885 on OpenAlexvenueno aff
Lindsay Farmer

Bibliographic record

VenueUniversity of Toronto Law Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsCivilityCriminal lawLawCivil law (Civil law)Civil societyModernitySocial orderSociologyPolitical sciencePublic lawPolitics

Abstract

fetched live from OpenAlex

This article explores the meaning of the term ‘civil order’ by asking what it means to claim that criminal law is only ‘intelligible’ from the perspective of civil order. In Part II, I examine different possible meanings of the term intelligibility. In Part III, I go on to look at ways of understanding the term ‘civil order,’ arguing that it must be seen primarily as a historically situated question – that is to say, both the question of what amounts to order, and conceptions of civility, depend on particular historical contexts. I illustrate this point by looking at how order was conceived of as a specific kind of problem in modernity and how this has shaped the understandings of the scope and the function of the criminal law. In Part IV, I look at a neglected dimension of this modern understanding of civil order by looking at the way that the relation between the market and the criminal law – that is to say, between a sphere of social life ordered by contract or civil law and those spheres ordered by criminal law – has been conceived of in modernity. I conclude that this distinction between market and civil society underpins the thinking about the proper scope of the criminal law but that, if we are properly to understand the role of criminal law in securing civil order, it is necessary to reflect not only on the civility of civil order but also on how we understand the scope of civil order in modern society.

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 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 score0.970
Threshold uncertainty score0.900

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.245
Teacher spread0.226 · 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 teacher head, 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

Citations7
Published2020
Admission routes1
Has abstractyes

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