La cyberdémocratie québécoise : Twitter bashing, #VoteCampus et selfies
Bibliographic record
Abstract
Les potentialités démocratiques des médias sociaux font désormais partie des programmes de recherche de plusieurs politologues. Toutefois, la première analyse de l’utilisation de Twitter en contexte politique au Canada ne remonte qu’à 2010. Nous contribuons à cette récente littérature en explorant les manières dont les candidats aux élections générales québécoises de 2014 utilisent ce média. Pour les cyber-optimistes, la twittosphère peut faciliter les interactions avec les politiciens, augmenter l’accès à l’information et encourager la participation politique. En revanche, les cyber-pessimistes estiment que les espaces numériques servent essentiellement les professionnels du marketing politique. Pour analyser ces prétentions dichotomiques, nous avons analysé le contenu de plus de 13 000 gazouillis de candidats siégeant à l’Assemblée nationale du Québec. Nos résultats démontrent qu’à l’exception des candidats de Québec solidaire (dont lestweetsse démarquent de manière statistiquement significative), les politiciens rattachés aux trois partis principaux du Québec ont surtout utilisé Twitter à des fins de marketing politique, notamment dans une stratégie de campagne négative.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".