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Record W4404942250 · doi:10.13130/1971-8543/14395

Culturally Motivated Crimes: the Cultural Test in the Italian Jurisprudence. A Comparative Study

2020· article· en· W4404942250 on OpenAlexaboutno aff
Sophie Charlotte Monachini

Bibliographic record

VenueRiviste UNIMI (Università degli studi di Milano) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsJurisprudenceTest (biology)CriminologyPsychologyPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

SUMMARY: 1. Key terms and definitions - 2. Two ways to look at culturally motivated crimes: the case of the kirpan in the Italian and in the Canadian jurisprudence - 3. Moving forward: the Italian Court of Cassation n. 29613/2018 - 3.1. Cultural tests: a comparative insight - 3.2. The cultural test in the Italian judgment of 2018 - 4. Final remarks. ABSTRACT: The aim of this article is to analyze culturally motivated crimes with a comparative focus and a case law approach. First of all, a set of definitions is given to have a common and shared understanding of the topic at stake. What is culture and what do we mean by culturally motivated crimes? Secondly, a comparative case-law study helps to focus the possible different solutions given to the cultural factor in criminal law. In fact, some legal systems -such as the Canadian one - appear to be more willing than others to make concrete accommodations of cultural differences. Lastly, the latest Italian Court of Cassation suggests a possible direction to be taken to better treat culturally motivated crimes in today’s multicultural society. What does it mean for criminal law to be culturally sensitive? How can courts take into consideration cultural factors through a proper legal method? These are the main questions on which criminal doctrine has focused its attention trying to reshape a criminal model which can be responsive to those different cultural values, needs and interests.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.315
Teacher spread0.254 · 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 designQualitative
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

Citations0
Published2020
Admission routes1
Has abstractyes

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