Cataloguer le cyberespace : le défi des ressources électroniques
Bibliographic record
Abstract
Le catalogage des ressources accessibles dans le réseau Internet est problématique. Plusieurs initiatives américaines ont permis d’identifier des problèmes majeurs, par exemple les projets Ressources Internet et Intercat d’OCLC, le projet d’encodage de texte (TEI) et le projet Dublin Core d’OCLC. Par ailleurs, des outils tels les URC, les URN et les PURL ont été conçus pour aider les bibliothécaires à mettre de l’ordre dans le chaos existant dans Internet. L’article décrit ces projets et outils pour identifier les principaux problèmes auxquels les bibliothécaires doivent faire face dans le traitement de ces ressources. Enfin, les niveaux de compétence dont les bibliothécaires au catalogage auront besoin à l’avenir et leur rôle dans l’établissement de normes pour l’échange d’information dans la communauté Internet sont brièvement discutés.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".