Les enjeux de la réputation à l’ère du numérique
Bibliographic record
Abstract
Si la réputation prend aujourd’hui une importance de premier ordre pour les entreprises, c’est en grande partie en raison du développement de leur identité numérique, identité dont la singularité est d’être construite sur les traces et les perceptions existantes sur les réseaux numériques sans aucun héritage généalogique. Thierry Belleguic, Jérôme Coutard et Milad Doueihi (Université Laval de Québec) évoquent ici les enjeux, risques et défis associés à l’existence de cette identité numérique et expliquent comment celle-ci est consubstantielle à la traçabilité et à la e-réputation. A travers quelques exemples tirés du monde de l’entreprise autant que du champ politique, les auteurs concluent au besoin impérieux pour tout acteur présent numériquement de gérer et de veiller à son image sur la toile.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".