The principle of proportionality and the fundamental right to personal data protection : the biometric data processing
Bibliographic record
Abstract
L'article aborde les limites du droit a la protection des donnees personnelles, en considerant le principe de proportionnalite. Il analyse le besoin d'accessibilite et la regle de predictibilite, le but legitime, en s'attardant sur la priorite donnee a la liberte d'information par rapport a la protection des donnees personnelles. L'article se penche egalement sur le principe de proportionnalite et la recherche d'equilibre. Le premier peut se diviser en trois sous-principes : la justesse, la necessite et la proportionnalite dans le sens strict du terme. L'article explique un cas de limite du droit fondamental a la protection personnelle : le traitement des donnees biometriques. Il analyse egalement la donnee briometrique et le droit a l'integrite physique, la protection de la vie privee, en s'attardant sur les principes de qualite.
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 | 0.029 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".