Faire mémoire sur internet. Les réseaux sociaux et sites de commémoration induisent-ils de nouveaux rapports à la mort ?
Bibliographic record
Abstract
L’apparition de nouvelles technologies, tout d’abord par les réseaux sociaux sur Internet (texte, image, vidéo, émoticône), puis via les applications pour les téléphones intelligents ( Smartphones ) a profondément modifié les façons de communiquer, notamment des jeunes générations. Ces nouvelles technologies de la communication ont-elles aussi transformé les rapports à la mort et au deuil ? De nouvelles pratiques sont-elles apparues depuis la démocratisation d’Internet et des téléphones intelligents ? Dans cet article, on s’interroge sur deux types d’expression du deuil, dans un premier temps, sur la façon dont les individus expriment leur deuil dans les réseaux sociaux numériques et d’autre part sur la façon dont la presse et les journalistes créent des mémoriaux pour les victimes lors d’événements traumatiques (attentats, catastrophes).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".