Discours et pratiques de vérification chez les journalistes primés
Bibliographic record
Abstract
Les journalistes subissent désormais la concurrence d’un nombre quasi infini de sources sur Internet. Sur quelles bases peuvent-ils aujourd’hui réclamer l’attention et la confiance du public? L’exigence d’exactitude apparaît comme fondamentale et universelle, mais, au-delà de l’importance que les journalistes accordent d’emblée à la vérification, quelles sont les règles qui régissent leurs routines quotidiennes? Cette étude examine à la fois les pratiques et le discours des journalistes de quotidiens canadiens, dans le but de contribuer au développement de meilleures pratiques de vérification journalistique. Nous y présentons les résultats de quatre entretiens individuels réalisés auprès de journalistes récipiendaires ou finalistes de prix d’excellence afin de reconstruire les stratégies de vérification utilisées par les reporters et de comprendre leur point de vue sur les normes qui les régissent.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.092 | 0.003 |
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; both teacher heads agree on what is shown here.
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".