Le livre numérique au Québec : le cas des emprunts aux bibliothèques publiques autonomes
Bibliographic record
Abstract
L’essor récent de l’offre et de la demande de livres numériques a précipité le développement des collections des bibliothèques publiques en mode immatériel. Les prêts de livres numériques effectués par les bibliothèques sont le reflet des comportements d’emprunt des usagers. La nature et l’amplitude des prêts de livres numériques au Québec demeurent méconnues. Cette relative méconnaissance origine de la nature nouvelle du phénomène numérique et de l’absence d’études en la matière. Cet article dresse un portrait des prêts de livres numériques effectués par les bibliothèques publiques autonomes du Québec à l’aide des données de 2013 et 2014. Il en ressort que l’emprunt de livres numériques est une pratique essentiellement urbaine, qui concerne davantage les livres pour adultes, de fiction qui plus est.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".