Bibliographic record
Abstract
Subject access in Canada, whether through subject headings, classification, thesauri or other structures, is dominated by systems originally created in the United States. Building on a 2019 literature review that identified current subject access systems and developing projects in the Canadian context, this paper will explore the patterns of divergence and convergence between systems and across borders. As subject access systems from the United States do not meet all the needs of Canadian scholarship, next steps include considering how these gaps and distortions impact Canadian scholarship and what institutions in Canada are doing to create systems consistent with their values. L'accès par sujet au Canada, que ce soit par le biais de vedettes-matière, de classifications, de thésaurus ou d'autres structures, est dominé par des systèmes créés à l'origine aux États-Unis. S'appuyant sur une analyse documentaire de 2019 qui a identifié les systèmes d'accès par sujet actuels et le développement de projets dans le contexte canadien, ce Le document explorera les modèles de divergence et de convergence entre les systèmes et au-delà des frontières. Étant donné que les systèmes d'accès par sujet des États-Unis ne répondent pas à tous les besoins de la recherche canadiennes, les prochaines étapes consistent à examiner l'impact de ces lacunes et distorsions sur la recherche canadiennes et les efforts des institutions canadiennes visant à créer des systèmes cohérents avec leurs valeurs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.030 |
| Science and technology studies | 0.038 | 0.025 |
| Scholarly communication | 0.022 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".