La stratégie numérique des partis politiques québécois lors de la campagne électorale de 2018
Bibliographic record
Abstract
À travers une série de 10 entrevues semi-structurées menées auprès du personnel de campagne de quatre partis politiques québécois (Coalition avenir Québec, Parti libéral du Québec, Parti québécois, Québec solidaire), ce mémoire trace un portrait des pratiques et des stratégies numériques mises en oeuvre dans le cadre de la campagne électorale de 2018. Nous constatons que les partis étudiés présentent un très haut niveau de déploiement du numérique, intégrant autant que faire se peut les bons coups observés à l’international. Ainsi, les partis ont mis en place de nouvelles plateformes pour compiler des données, mobiliser leurs militants, et optimiser leurs publicités. Nous concluons notamment que le numérique affecte la distribution des ressources en campagne électorale, la structure des organisations ainsi que le niveau de raffinement des messages politiques. Les nouvelles technologies sont centrales à l’exercice de la communication politique au Québec, et aucun parti ne semble se soustraire à cette convergence numérique.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".