“Euronoir Ltd.” ? Deux décennies d’import/export de fiction criminelle vues de France
Bibliographic record
Abstract
La nbuleuse notion d' Euronoir jouit d'une certaine reconnaissance mdiatique depuis l'essai journalistique de Barry Forshaw, qui l'a en quelque sorte labellise 1 . Souhaitant questionner de manire critique cette appellation, qui dans sa saisie unifiante pourrait relever pour partie de la prophtie auto ralisatrice, nous viserons dans cet article en prouver la validit, en apprhendant les phnomnes amalgams sous cette tiquette par un distant reading outill numriquement 2 . Conformment aux perspectives dessines de manire pionnire par Franco Moretti 3 et approfondies par son quipe de Stanford University 4 , les graphiques, cartes et statistiques, labors partir d'un questionnement raisonn du big data disponible, peuvent en effet constituer selon nous des instruments analytiques qui dmontent l'oeuvre d'une faon inhabituelle et imposent de nouvelles tches l'intelligence critique 5 ; ces expriences , qui russissent si elles reposent sur un travail prliminaire d'abstraction et de quantification 6 , peuvent de fait permettre de porter un regard renouvel, sinon indit, sur des phnomnes massifs de production, diffusion et circulation des fictions mdiatiques, que le seul close reading textualiste peine saisir autrement que de manire parcellaire et intuitive.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".