Bibliographic record
Abstract
Tocqueville avait raison: les États-Unis donneraient le pouvoir à l’opinion. Facebook connecte chacun à ses proches, mais les réseaux sociaux renforcent le cloisonnement de la société. Si des milliers de personnes font circuler des polémiques, le succès d'audience renforce les peurs et la vindicte. L'élection de Trump révèle un fait d'éditorialisation que masquait l’euphorie des promoteurs de Facebook. Faute d’un cadre approprié pour une conversation démocratique, l'entre-soi des réseaux personnels caricature la vie sociale. Facebook commence juste à réagir. Pour éviter de devenir Fakebook, il doit combiner la liberté d'expression à des dispositifs éditoriaux professionnels et stimuler les initiatives civiques. Et assumer des responsabilités qu'il a voulu ignorer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".