Guide sur les conditions et bonnes pratiques pour la mise en place d’une identité numérique nationale
Bibliographic record
Abstract
Ce guide poursuit deux objectifs, soit (1) le développement des connaissances sur les conditions et bonnes pratiques pour la mise en place d’une identité numérique nationale ainsi que (2) la mise en évidence dans une perspective critique des enjeux, impacts et conséquences envisageables de l’implantation d’un projet d’identité numérique nationale sur la société, les organisations et les individus. L’étude de cette question s’inscrit dans la mission plus large du Pôle d’expertise en cybersécurité et impacts sociétaux de l’Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique (OBVIA) d’alimenter la recherche sur la cybersécurité au Québec considérant que l’identité numérique a pour objectif d’assurer les principes fondamentaux de la sécurité de l’information, notamment par la protection des renseignements personnels.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".