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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".