Bibliographic record
Abstract
Quel est l’état de la question en indexation automatique de monographies ? Bien que les premières tentatives d’indexation automatique datent du début des années 1960, elles n’ont toujours pas abouti à des systèmes satisfaisants du point de vue des indexeurs professionnels. Pourtant il y a lieu de s’interroger sur les possibilités actuelles d’indexation automatique, compte tenu du nombre croissant de documents numériques pour lesquels il serait intéressant de fournir un index comme celui qu’on trouve à la fin d’un livre ( back-of-the-book index ). En outre, les quinze dernières années ont vu des innovations importantes dans le domaine du traitement automatique des langues (TAL), qui pourraient avoir des applications avantageuses pour l’indexation automatique de monographies. Cet article propose de définir la problématique et d’identifier les nouvelles pistes de solutions à explorer afin de dépasser les performances des systèmes actuellement offerts pour l’indexation automatique de monographies.
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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".