Gérer sa réputation à l’heure des réseaux sociaux : un nouveau défi pour les entreprises
Bibliographic record
Abstract
Chaque jour, cinq millions de messages circulent sur Twitter. Si l’on considère que la longueur d’un tweet (140 caractères) équivaut au message contenu dans un biscuit chinois, ces gazouillis représenteraient plus de 2 500 tonnes de pâte croustillante. Cette avalanche de mots peut toutefois s’avérer bien moins inoffensive que des prédictions un peu simplistes. À l’heure des médias sociaux, une tempête peut se lever sans crier gare et écorcher au passage ce qu’une entreprise a mis des années à bâtir : sa réputation. Nestlé, PFK et Lassonde en savent quelque chose !
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".