La protection des infrastructures d’information critiques à travers le monde
Bibliographic record
Abstract
L’approche française en matière de protection des infrastructures vitales, précisée par Alain Coursaget dans ce numéro, est partagée par d’autres États qui ont en commun de dépendre de plus en plus de réseaux interconnectés, conférant une importance toute particulière aux infrastructures d’information. Parmi celles-ci, certains systèmes d’information seront qualifiés de « critiques » parce que leur interruption ou leur destruction pourrait causer un impact considérable sur la santé, la sûreté, la sécurité, le bien-être des citoyens, jusqu’au fonctionnement effectif de l’État ou de l’économie. Le Département Risques et Crises de l’INHESJ suit depuis plusieurs années les travaux de l’OCDE sur la thématique de la gouvernance de crise. Parmi les études récemment réalisées, celle sur l’analyse comparative des politiques de protection des infrastructures critiques d’information est particulièrement intéressante. Elle concerne sept pays : Australie, Canada, Corée, Japon, Pays-Bas, Royaume-Uni et États-Unis.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".