Bibliographic record
Abstract
Crime, Memory, and ViolenceFor over thirty years, modern Italy was plagued by ransom kidnappings perpetrated by bandits and organized crime syndicates.Nearly 700 men, women, and children were abducted from across the country between the late 1960s and the late 1990s, held hostage by members of the Sardinian banditry, Cosa Nostra, and the 'Ndrangheta.Subjected to harsh captivities and psychological abuse, the victims spent months and even years in isolation while law enforcement and the state struggled to find them.Ransom Kidnapping in Italy examines this Italian criminal phenomenon.Alessandra Montalbano argues that abduction is a key vantage point from which to understand modern Italy: it troubled the law, terrified society, ignited juridical and parliamentary debates, and mobilized citizens.Bringing together archival and media materials with the victims' accounts and diverse forms of cultural response, the book examines ransom kidnapping through the lenses of historiography, law, literary criticism, trauma studies, phenomenology, and political philosophy.Ransom Kidnapping in Italy traces how and at what price Italians became aware of living in a country that was being blackmailed by criminal organizations that arguably jeopardized the nation even more than terrorism.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.721 | 0.515 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".