Les organisations : sentinelles aveugles de la sécurité des données personnelles ?
Bibliographic record
Abstract
Dans le courant de l’année 2007, un agent public a envoyé un mail à un de ses homologues d’un pays sensible, sans se rendre compte qu’il avait associé au message quelques documents ultra-confidentiels en pièce-jointe. Cet exemple confirme deux tendances que Benoit Dupont dessinent dans son étude : l’administration publique est moins bien protégée que les entreprises tandis que la disparition d’informations dans les services gouvernementaux résulte davantage de l’erreur humaine que du piratage. L’auteur, en procédant à une analyse sectorielle du phénomène, nous révèle que la disparition de données personnelles touche de manière très différente chaque secteur d’activité. Il en conclut que chaque organisation doit procéder à une analyse précise de sa situation et suggère un éventail de techniques de prévention devant permettre aux entreprises de mieux sécuriser leurs données personnelles.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".