LES MÉDIAS SOCIAUX ET LE BONHEUR : LE CAS DE FACEBOOK
Bibliographic record
Abstract
Les réseaux sociaux ont rapidement changé la façon dont les gens interagissent entre eux. Le réseau social le plus populaire est Facebook, avec un nombre croissant de personnes qui consacrent de plus en plus de temps sur ce site chaque jour. Dans cet article, nous discutons de l’impact de l’utilisation de Facebook sur le bonheur. La revue de littérature révèle que le bonheur serait influencé différemment selon une utilisation passive ou active de Facebook. L’utilisation active de Facebook stimule le capital social et le sentiment de connexion, qui, à leur tour, ont un impact positif sur le bonheur. L’utilisation passive de Facebook mène souvent à une hausse de la comparaison sociale et de l’envie, qui, à leur tour, ont un impact négatif sur le bonheur. Les gens sont en général plus passifs qu’actifs sur Facebook et ainsi, leur utilisation du site tend à diminuer plutôt qu’à augmenter leur bonheur. Nous terminons cet article en discutant de pistes de recherches futures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".