Mafiacraft: An Ethnography of Deadly Silence, by Deborah Puccio-Den
Bibliographic record
Abstract
I n Mafiacraft, Puccio-Den provides a novel, thoughtful, and innovative methodological approach to understanding the Sicilian mafia and anti-mafia movement within one ethnographic frame.She asks: How do we hear the noisy silence of Cosa Nostra?Her answer derives from an ethnographic analysis of the cultural continuum and political intersection between mafia and anti-mafia spheres in Sicily.Silence matters, Mafiacraft suggests.Whether it be through the omertà (the mafia code of silence), the cover-up silencing of the state actors that permit the mafia to operate and intrude into the public sphere, or the antimafia strategy to "break the silence" around mafia worlds.Indeed, by framing the mafia as a constitutive social process, Mafiacraft explores how the Cosa Nostra crafted a criminal organization shaped by power, rituals, and symbolism, but also how the mafia and the anti-mafia have shaped the Sicilian social order.When it comes to researching criminal groups and violent social actors in general, fieldwork and data collection are challenging and, at times, dangerous.Nevertheless, observing and gathering data is as important as in other contexts where the researcher's visibility is not as consequential.Mafiacraft provides a model for how to collect and interpret ethnographic data through the fog of social silence.If silence binds the Cosa Nostra together within the Sicilian social order, breaking that silence renders the mafia visible and provides political agency to those who take a stand against it.Puccio-Den proves that silence gives way to meaningful noise if the correct analytical lens is used.Silence, both a sensory and an analytic experience, enables her to develop a comprehensive understanding of the Sicilian mafia, but, more generally, of crime and violence as social and cultural phenomena.Ethnographic work provides an ideal approach to unpacking the presence and absence of mafia actors in Sicilian private and public spheres.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".