Gilles-Éric Séralini ou la transgression des médiations « traditionnelles » du savoir ?
Bibliographic record
Abstract
Gilles-Éric Séralini est un biologiste français connu pour ses prises de position radicales contre les OGM et les pesticides, en particulier le glyphosate. En 2012, la publication d’un de ses articles a causé une importante controverse scientifique associée à une très forte polémique médiatique : « l’Affaire Séralini ». Cet article interroge le traitement médiatique de « l’Affaire Séralini », tout comme les stratégies de publicisation et de médiatisation originales adoptées par Gilles-Éric Séralini et critiquées de nombreux chercheurs et journalistes. Au-delà d’une transgression possible des médiations traditionnelles du savoir, nous observons également les stratégies de communication sensible déployées par Monsanto pour faire retirer l’article tout comme les répercussions de l’affaire sur la figure du chercheur dans les médias et la communauté scientifique.Gilles-Éric Séralini is a French biologist known for his radical positions against GMOs and pesticides, in particular glyphosate. In 2012, the publication of one of his articles caused a major scientific controversy associated with a very strong media controversy: “The Séralini Affair”. This article questions the media treatment of the “Séralini Affair”, as well as the original advertising and media strategies adopted by Gilles-Éric Séralini, criticized by numerous researchers and journalists. Beyond a possible transgression of traditional knowledge mediations, we also observe the sensitive communication strategies implemented by Monsanto to have the article withdrawn as well as the repercussions of the case on the figure of the researcher in the media and in the scientist community.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".