Discours des réseaux sociaux : enjeux publics, politiques et médiatiques
Bibliographic record
Abstract
Lors de situations de crise, l’expression d’excuses sur les médias socionumériques (MSN) permet aux politiciens offenseurs de communiquer directement avec leurs publics, sans le filtre journalistique, en vue de rétablir la confiance avec ceux-ci ou, du moins, de reprendre le contrôle de la crise. À partir d’un corpus d’excuses produites entre 2013 et 2016 par des politiciens canadiens sur les MSN, en contexte parlementaire et de relations de presse, l’analyse vise, d’une part, à identifier si les excuses en contexte numérique s’accompagnent d’interactions avec les internautes, dans une logique conversationnelle, ou si, au contraire, elles s’inscrivent dans une logique de diffusion et, d’autre part, à comparer les modalités d’expression des excuses dans ces contextes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".