Animation et gestion des communautés en ligne
Bibliographic record
Abstract
La question des communautés en ligne occupe une part non négligeable de la littérature scientifique en communication et, plus généralement, mobilise des connaissances dans les autres sciences humaines et sociales voisines, telles que la sociologie, l’anthropologie, la psychosociologie ou les sciences de gestion. Nous remarquons pourtant que l’angle utilitariste reste dominant bien que sa critique peut être retrouvée dès les années quatre-vingt. L’avènement massif d’un web 2.0, web social ou web participatif n’a pas aidé en la matière. Les pratiques managériales y soumettent les dynamiques sociales à des logiques d’entreprise. Entre management et animation, le présent dossier propose quelques clefs de lecture sur l’évolution de ces collectifs cadrés par le numérique. On-line communities are an important trend in scientific literature, in communication as well as in other neighbouring social sciences, such as sociology, anthropology, psychosociology or business administration. We note, however, that the utilitarian angle remains dominant although its criticism can be found as early as the eighties. The massive advent of a web 2.0, a social or participatory web has not helped in this matter as managerial practices submit social dynamics to corporate logics. Between management and animation, we share some keys to understanding the evolution of these digitally framed collectives.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".