Élection 2018 : Une nouvelle étape dans la pratique du marketing politique au Québec
Bibliographic record
Abstract
Résumé Cet article fait état des pratiques marketing de quatre partis politiques (Coalition avenir Québec, Parti libéral du Québec, Parti québécois, Québec solidaire) en vue de l’élection générale québécoise de 2018. La couverture médiatique sur la pratique du marketing politique au Québec laissait présager une adoption plus marquée de l'approche marketing en 2018, notamment grâce à une utilisation soutenue des données numériques. Pour vérifier cette hypothèse, des entrevues semi-dirigées ont été menées auprès du personnel de campagne de ces formations. Nos résultats stipulent que la planification électorale se fait bel et bien dans un esprit de marketing politique. Les partis pratiquent toutefois un marketing partiel : ils mobilisent principalement l'intelligence de marché à des fins tactiques. Le numérique entraîne un raffinement de cette approche publicitaire. Finalement, l'application du concept dedata-driven campaignn'est pas encore pleinement intégrée au Québec.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".