Do Provenance-Based Classification Schemes Have a Role in Libraries and Information Centres? The Case of Classifying Government Publications
Bibliographic record
Abstract
Libraries and information centres use often multiple classification schemes for organizing their collections. In Canadian full depository libraries government publications can be organized in collections using a government publishing office's own notation, knowledge organization notation, or other notational scheme designed especially for government publications. Provenance-based schemes such as CODOC are attractive for their universality and for work-related purposes that may be influenced by financial challenges. However, libraries that use multiple notations for government publications may open the potential for intellectual disruption to information retrieval practices in either physical or virtual browsing.
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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 | 0.106 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.022 | 0.035 |
| Scholarly communication | 0.038 | 0.141 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".