Perceptions et attitudes des professionnels des bibliothèques face aux approches d’amélioration continue
Bibliographic record
Abstract
L’objectif de cet article est de mieux comprendre les attitudes et les perceptions des professionnels dans le milieu des bibliothèques du Québec à l’égard des approches d’amélioration continue. Une enquête utilisant la méthode du questionnaire rempli par voie électronique a été déployée. Si les approches d’amélioration continue abondent vers des visées qui sont en adéquation avec celles des bibliothèques québécoises, les attitudes et les perceptions des professionnels de ce milieu demeurent prudentes, voire négatives, envers ces approches. Cet état de fait pourrait prendre sa source dans la culture organisationnelle de même que la méconnaissance des approches d’amélioration continue. Aussi, l’ouverture au changement des professionnels dans le milieu des bibliothèques québécoises s’avère positive de façon générale, mais prudente dès lors qu’il s’agit d’un changement affectant le travail des professionnels eux-mêmes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.059 | 0.097 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".