Bibliographic record
Abstract
Comment traiter et analyser à l’aide de l’ordinateur des résultats d’interrogation de bases de données bibliographiques pour étudier les caractéristiques de la littérature sur un sujet donné ? C’est la question à laquelle cette recherche tente de répondre en prenant pour sujet les aspects cognitifs en sciences de l’information. La démarche réalisée se compose de quatre étapes principales soit : 1) la collecte des données (références bibliographiques extraites de LISA et de ERIC); 2) le traitement (tri, codification, uniformisation et fusion) des données recueillies; 3) l’analyse des données traitées pour en extraire les caractéristiques de la littérature; et 4) la présentation et l’utilisation des résultats de l’analyse. Lors de l’application de cette méthode, deux logiciels ont surtout été utilisés : un traitement de texte (WordPerfect) et un système d’analyse de texte (SATO).
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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".