Entre transparence des sources et entre-soi : une critique du fact-checking du débat de l’entre-deux tours de la présidentielle française de 2017
Bibliographic record
Abstract
Le débat de l’entre-deux tours de la présidentielle française de 2017 est révélateur des ambiguïtés du fact-checking quand il prétend dénoncer les mensonges propagés par les acteurs publics. Drapées dans un discours de vérité, les pratiques de fact-checking visent d’abord à identifier le faux plus qu’à dire le vrai. Elles délèguent l’établissement de la vérité à des sources fiables que le fact-checker pourra mobiliser. L’analyse révèle toutefois qu’il s’agit d’abord de sources institutionnelles considérées comme collectivement légitimes. Le fact-checking déploie ainsi une approche potentiellement conservatrice de l’information journalistique qu’il applique ensuite à l’ensemble des propos tenus dans l’espace public.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".