Risque digital, cyber risques et risques liés au RGPD : la question de l’assurance
Bibliographic record
Abstract
Dans leur portrait des emprunteurs de livres numériques (LN) au sein de Bibliothèque et Archives nationales du Québec (BAnQ), Lapointe, Luckerhoff et Niort (2023) les ont comparés aux emprunteurs de livres physiques (LP) et aux emprunteurs hybrides. L’exercice a permis de dégager des tendances et de soulever des questions, notamment : quel est le vécu des emprunteurs de LP qui font la transition vers le LN ? Comment se déroulent les expériences d’emprunts ? Quel sens les emprunteurs donnent-ils à la bibliothèque en tant que lieu ? Dans le cadre de la recherche dont fait état le présent article, nous avons animé plus de 70 entretiens individuels et de groupe auprès d’emprunteurs et de non-emprunteurs de BAnQ et d’autres bibliothèques publiques afin de mieux comprendre leur vécu à l’égard des LP et des LN, des lieux du livre comme la bibliothèque publique — y compris la Grande Bibliothèque de BAnQ —, leurs perceptions de l’ensemble de ces réalités ainsi que leurs expériences d’emprunts.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".