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Record W4407555258 · doi:10.1017/lsr.2025.12

¿Cómo no tener que saber? Tecnicismos jurídicos y delitos flagrantes en Santiago, Chile

2025· article· es· W4407555258 on OpenAlexaff
Javiera Araya-Moreno

Bibliographic record

VenueLaw & Society Review · 2025
Typearticle
Languagees
FieldSocial Sciences
TopicCriminal Justice and Penology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Resumen Sobre la base de información recolectada en tribunales de primera instancia y en una unidad del Ministerio Público en Santiago, Chile, en este artículo se explora la manera en que el sistema de justicia penal trata los delitos considerados “flagrantes.” Citando literatura sobre tecnicismos jurídicos, describo cómo los delitos flagrantes se construyen a través de prácticas que hacen posible para los actores involucrados evitar referirse directamente a los supuestos hechos. Desde su identificación en las calles por parte de policías a su asignación a otra unidad del Ministerio Público, los delitos flagrantes se definen por una manera específica de aproximarse a los supuestos hechos, la que consiste en prácticas organizacionales y documentales específicas. Estas prácticas contrastan con el rol marginal de la detención “en flagrancia” según el Código Procesal Penal. Como un tecnicismo, el carácter flagrante del delito expresa ciertas suposiciones epistemológicas respecto a cómo determinar lo que pasó y lo que exactamente lo constituye. Más específicamente, el carácter flagrante expresa suposiciones sobre lo que, por el momento, no puede ser sabido y puede, por tanto, ser ignorado a través del proceso burocrático y judicial.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.348
Teacher spread0.335 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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