Towards a definition of Mediterranean Noir or Crime in the Mediterranean: Mediterranean noir or Mediterranean crime fiction?
Bibliographic record
Abstract
This article argues that Mediterranean crime fiction is not simply a subgenre that showcases criminal organisations and beautiful landscapes, but also a formulation that overcomes the exclusionary European borders, and makes the “parent” label Euronoir more inclusive. In order to prove this point, this article analyses Andrea Camilleri’s Il ladro di merendine (1996) and Jean Claude Izzo’s Total Khéops (1996) through the lens of transculturality (Welsch) and the idea of “third space” (Bhabha). It shows how, with their reference to a common Mediterranean culture and history, these novels shape transcultural spaces where human beings coexist and adapt to each other. Both novels make use of the concept of “homecoming” as a counter-narrative for the present anti-immigration rhetoric, and represent the Mediterranean as a Mare Nostrum which, according to Paolo Rumiz’s formulation, is a space shared by those who inhabit it, where inhabiting does not necessarily coincide with belonging or possession1. In doing so, Il ladro di merendine and Total Khéops represent Mediterranean cities as places where the encounter supersides conflict, overcoming the exclusionary mechanisms of the nation-state.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".