Telling about the Mafia: Resarch, Reflexivity, Representation
Bibliographic record
Abstract
This edited Symposium of the journal "Sociologica" (http://www.sociologica.mulino.it/main/index) is aimed at collecting and comparing the voices of some leading scholars of the Mafia coming from the social sciences – scholars whose research has contributed to the development of Mafia studies as a veritable specialty in the social sciences over the last forty years: the German sociologist Henner Hess, the American anthropologists Jane and Peter Schneider, the Canadian political scientist Filippo Sabetti, the Italian (but UK-based) sociologist Diego Gambetta, and the Sicilian social researcher and writer Umberto Santino. I invited these authors to write a personal account of their research experience as Mafia scholars, insisting on four topics: how and why did they chose the Mafia as an object of investigation; which contribution to their sociological gaze and imagination this research provided and what the Mafia as a sociological object may contribute to the sociological imagination in general; what impact has their work had on Mafia studies and scholarship at large. Last but not least, what impact according to them their previous life experiences and values had on them choosing how to study the Mafia.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.054 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".