AUTOMATING A RETROSPECTIVE CANADIAN UNION CATALOG: A PROPOSAL / UNE PROPOSITION POUR CREER UN CATALOGUE COLLECTIF CANADIEN DES DOSSIERS-MACHINE RETROSPECTIFS
Bibliographic record
Abstract
The paper describes methods of linking by machine, the machine-readable data base of the HPB project, the cataloging records of the National Union Catalog: pre-1956 Imprints3 and selected bibliographical tools. One result, in the form of a machine-readable register of Canadian locations, can be selectively expanded into a substitute in machine-readable form for the present Canadian Union Catalog (CANUC). The theory and practice underlying the creation of the HPB main file and its highly specialized collocation files are described and possibilities for integrating the project into evolving systems for universal bibliographical control of retrospective materials suggested. On explique dans ces pages des méthodes pour lier par ordinateur les donnés lisibles dans la machine du projet HPB, les dossiers du catalogue collectif National Union Catalog: pre-1956 Imprints et les renseignements bibliographiques pris dans des bibliographies choisies. Un des resultats, en forme d’un registre de sigles des bibliothèques possédantes, est capable de devenir peu à peu un index établi par ordinateur pour développer un Catalogue collectif automatisé de dossiers retrospectifs qui remplacera les tiroirs de fiches de la Bibliotheque nationale. On explique aussi la théorie et la pratique du projet HPB, c'est â dire le fichier principal (main file) et les fichiers d’arrangement spécialisés (collocation files), avec ses possibilités de liaison avec les systèmes de contrôle bibliographique universelle qui sont en train de se développer actuellement.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".