Bibliographic record
Abstract
L’optimisation de l’efficacité d’un système d’information nécessite la mesure de ses performances. Or, l’évaluation d’une bibliothèque requiert l’emploi de données bibliométriques. Après avoir défini la nature des données bibliométriques, l’auteur souligne leur importance pour une analyse approfondie des situations documentaires complexes existant dans les bibliothèques. Il démontre l’utilité des données bibliométriques dans la gestion des systèmes documentaires à partir de deux modèles analytiques : le modèle de Buckland qui permet de déterminer le nombre d’exemplaires à acquérir pour un titre donné et un modèle qui sert à mesurer le « taux de pénétration » d’une bibliothèque dans son environnement.
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 | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.048 | 0.170 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".