L’utilisation des groupes de discussionen marketing commercial et social
Bibliographic record
Abstract
Les méthodes quantitatives ont traditionnellement été privilégiées en marketing puisque plus fiables et « généralisables » à l’ensemble du marché. Toutefois, certaines questions relatives au comportement des consommateurs ont conduit à l’utilisation de techniques plus souples de recherche, essentiellement les groupes de discussion. Alors que la souplesse était appréciée, les exigences de la recherche en marketing commercial ont par contre transformé la technique en une procédure fortement structurée. Or, récemment, une application moins orthodoxe a permis de soulever des aspects particuliers de la pensée des individus qu’il aurait été impossible de déceler si nous nous en étions tenus à la façon traditionnelle du marketing commercial de mener les groupes de discussion. C’est basé sur l’observation de plus de 300 groupes de discussion, menés pour les campagnes de publicité du Ministère de la santé et des services sociaux du Québec, que les auteurs présentent leurs réflexions.
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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 | 0.155 | 0.302 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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; 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".