Bibliographic record
Abstract
Canada has a long history of adapting United States subject access tools, including the Library of Congress Classification (LCC), Library of Congress Subject Headings (LCSH) , the Dewey Decimal Classification, and the Sears List of Subject Headings , to meet the specific needs of Canadians. This paper addresses the extensions to these American tools for English-speaking Canadians. While the United States and Canada have many similarities, differences exist that require changing terminology and providing greater depth and precision in subject headings and classification for specifically Canadian topics. The major effort has been for Library and Archives Canada (LAC) systematically to provide extensions for LCC and LCSH for use within its cataloging records. This paper examines the history and philosophy of these Canadian efforts to provide enhanced subject access. Paradoxically, French-speaking Canadians may have found it easier to start from scratch with the Répertoire de vedettes-matière because of the difficult decisions for English-language tools on how much change to implement in an environment where most Canadian libraries use the American subject access tools. Canadian studies scholars around the world can use Canadian records, especially those maintained by LAC, to obtain superior subject access for Canadian topics even if they obtain the documents from other sources.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.022 |
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".