Évaluer la demande et les besoins en informations : pour des enquêtes croisées
Bibliographic record
Abstract
Résumé L’analyse et la satisfaction des besoins des usagers sont au cœur de la démarche des professionnels de l’information et documentation. Quels dispositifs mettre en place pour connaître la demande (exprimée) et évaluer les besoins (réels) d’une collectivité en informations documentaires ? Pour évaluer ensuite l’efficacité et la pertinence des produits et services proposés, les usages qui en sont faits et le degré de satisfaction des usagers ? Cet article propose un dispositif d’enquête qui vise moins à donner une méthode générale (dont l’application relève de spécialistes) qu’à permettre aux documentalistes de se familiariser avec les principaux moyens à mettre en œuvre pour entreprendre ou commanditer une telle enquête.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.059 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".