Évaluation de l’impact des stratégies de parrainage et co-marquage : une netnographie des réseaux sociaux
Bibliographic record
Abstract
Avec l’émergence du web 2.0 et des réseaux sociaux, le marketing est entré dans l’ère de l’hyper mesurabilité. Cependant une question demeure, quelles sont les stratégies d’alliance les plus efficaces sur les réseaux sociaux ? Pour répondre à cette question, une netnographie de 6 communautés virtuelles totalisant 554 725 fans sur Facebook a été réalisée. Les principaux résultats montrent que le parrainage d’athlètes est le plus performant au regard des commentaires et du nombre de « j’aime » générés. Aussi une approche qualitative a permis de mettre en lumière des réactions affectives de joie et de peur associées au parrainage.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".