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Record W4289780880 · doi:10.1111/lasr.12624

How to not have to know: Legal technicalities and flagrant criminal offenses in Santiago, Chile

2022· article· en· W4289780880 on OpenAlexafffund
Javiera Araya-Moreno

Bibliographic record

VenueLaw & Society Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAdjudicationCriminal justiceBureaucracyCriminal procedureCriminologyLawPolitical scienceCriminal lawUnit (ring theory)Criminal courtSociologyPsychologyInternational law

Abstract

fetched live from OpenAlex

Abstract Drawing on ethnographic data gathered in lower criminal courts and in one unit of the Public Prosecutor's Office in Santiago, Chile, I explore the way in which criminal offenses considered flagrant are treated by the Chilean criminal justice system. Citing the literature on legal technicalities, I describe how flagrant criminal offenses are constructed through practices that make it possible for the actors involved to avoid directly referring to the alleged facts. From their identification on the streets by police officers to their reassignment to a different unit of the Public Prosecutor's Office or their adjudication at a criminal court, flagrant criminal offenses are defined by a specific way of approaching the alleged facts, which is translated into specific organizational and documentary practices. The role of these practices contrasts with the apparently marginal role that the detention in flagrante delicto plays in the mechanics of criminal law. As a technicality, the flagrant character of a criminal offense conveys certain epistemological assumptions about how to determine what happened and what exactly constitutes the criminal offense. More specifically, it conveys assumptions about what cannot, for the moment, be known and that can, therefore, be ignored throughout the bureaucratic and judicial process.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.314
Teacher spread0.282 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes2
Has abstractyes

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