Local Colour: Investigating Social Transformations in Transcultural Crime Fiction
Bibliographic record
Abstract
Over the last twenty years, Italian “migration literature” has made significant contributions to the redefinition of the country’s literary and cultural scene. While the initial phase can best be conceptualized as a generic “micro-system” encompassing canonical genres such as (auto)biography and the Bildungsroman, more recently, narratives of migration have diversified radically, exhibiting a high degree of linguistic and genre experimentation. The defining feature of some of the more successful recent novelists lies in their active engagement with critical social and political issues that concern contemporary Italian society through the vehicle of the crime fiction genre. A case in point is provided by Algerian-born Amara Lakhous, whose four recent novels Scontro di civiltà per un ascensore a Piazza Vittorio (2006), Divorzio all’islamica a viale Marconi (2010), Contesa per un maialino italianissimo a San Salvario (2013) and La zingarata della verginella di Via Ormea (2014) all use strategies of genre hybridization (polyphonic migration narratives blended with giallo and noir structures) to problematize notions of citizenship and cultural identity. This article argues that borrowing the conventions of the giallo/noir enables Lakhous both to provide new insights into shifting constructions of “Italianness”/citizenship in a period characterized by the transition from national to transcultural communities and to accentuate the continuity of the dialogical relationship between the crime fiction genre and contemporary social reality.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".