Mafia Women: A Study on Language and Mental Representations of Women Engaged with Mafia Members
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".