Le fait divers criminel au prisme du web 2.0 : Le Grand Incendie, La Cité des mortes et Alma
Bibliographic record
Abstract
Parce que les renouvellements formels liés à l’émergence des webdocs sont propices, selon nous, à un enrichissement de notre perception et de notre compréhension du fait divers criminel, et par extension, de la souffrance et de la violence à l’œuvre dans notre société, cet article se veut une exploration — à travers quelques paradigmes de l’historienne Arlette Farge quant à l’écriture de l’histoire et de l’événement — de trois webdocs criminels, Alma de Miquel Dewever-Plana et Isabelle Fougère, Le Grand Incendie de Samuel Bollendorf et Olivia Colo et La Cité des mortes de Marc Fernandez et Jean-Christophe Rampal. Nous postulons ici que le webdoc, principalement caractérisé par son processus d’interactivité, favorise une narrativité multiple, indisciplinée, équivoque et à portée universelle, à rebours des stéréotypes narratifs, de l’ordonné, de l’unicité et de l’exotisme singulier. Autrement dit, le webdoc ne raconte pas une histoire mais la multiplicité fondatrice de celle-ci, tant du point de vue sémantique que formel.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".