L’indexation dans les bibliothèques parlementaires de Canberra, d’Ottawa et de Québec
Bibliographic record
Abstract
Le travail d’indexation dans une bibliothèque parlementaire peut varier énormément selon les ressources qu’on lui accorde ou encore les mandats qu’on lui confie. Il apparaissait intéressant de comparer l’expérience de la Bibliothèque de l’Assemblée nationale du Québec à celle de deux autres institutions : la Bibliothèque du Parlement fédéral à Ottawa ainsi que la Bibliothèque du Parlement australien de Canberra. Nous mettrons en parallèle ces trois réalités afin d’en dégager les similitudes et les différences. Quelles sont les ressources affectées au travail bibliothéconomique en milieu parlementaire ? Quels sont les outils mis à leur disposition ? Quels sont les produits réalisés et quelles sont les perspectives d’avenir ? Voilà autant de questions qui nous permettront d’établir un constat sur cet aspect parfois méconnu de ce travail.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.039 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".