Technologies d’exploitation du big data dans les organisations et transformations organisationnelles : une étude de cas au sein du Service de santé des armées françaises
Bibliographic record
Abstract
L’exploitation des données sanitaires dubig datavia des technologies de collecte, de visualisation et de communication a permis au Service de santé des armées françaises de créer une valeur stratégique. Par exemple, après le tsunami de 2004 en Asie, cette exploitation a permis d’anticiper les épidémies pour les forces envoyées en soutien humanitaire. Plus récemment, dans le contexte de l’épidémie d’Ebola en Afrique, des vies humaines ont pu être préservées. L’appropriation de tels outils a également entraîné des bouleversements dans l’organisation et la culture organisationnelle, notamment un décloisonnement spatio-temporel des activités et un gain de coordination.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.027 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".