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Record W2097658748 · doi:10.6000/1929-4409.2014.03.22

Mafia Women: A Study on Language and Mental Representations of Women Engaged with Mafia Members

2014· article· en· W2097658748 on OpenAlexvenueno aff
Adriano Schimmenti, Serena Giunta, Girolamo Lo Verso

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSicilianHonorPsychologySocial psychologySociologyCriminologyGender studiesLinguistics

Abstract

fetched live from OpenAlex

For a long time, women in the Mafia were considered victims who were unaware of the activities of the men in their families. However, it has recently been demonstrated that these women may play an important role in the organisation, particularly in the transmission of Mafia values. In this study, we explored the representations of self, relationships, and the Mafia world in women engaged with Mafia members. This was done by means of in-depth interviews and computer-assisted text analysis. A cluster analysis was applied to words used by the women in the interviews. Three clusters emerged that accounted for 85% of the principal contents of the interviews. These were interpreted as “representations of family”, “representations of social relationships”, and “ideals and values”. The analysis of words included in each cluster suggested that Mafia women are deeply involved in the transmission of traditional Sicilian values to their offspring. These ideals and beliefs are deeply rooted in the Mafia organisation and they involve attributing a central role to family, religion, and honor within the Mafia culture. Findings of the study could be positively used for developing appropriate preventative and social measures that may help these women change their ideals and beliefs related to the Mafia world, thus breaking the transmission of Mafia values.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.038
GPT teacher head0.350
Teacher spread0.312 · 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 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

Citations16
Published2014
Admission routes1
Has abstractyes

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