Using document delivery data for selecting medical titles in a large STM library
Bibliographic record
Abstract
This paper describes how a large science, technology and medicine (STM) library used document delivery data to support the selection of new journal titles in order to better meet the needs of document delivery clients. Over a nine-month period in 2004, the Canada Institute for Scientific and Technical Information (CISTI) undertook a review of its current serial subscriptions to determine whether they continued to meet the needs of researchers at the National Research Council of Canada (NRC) and the needs of document delivery clients both in Canada and internationally. This paper describes the study of the print serial collection and the findings. Bibliographic data on current serial titles in CISTI's Catalogue was compared with Document Delivery data on filled orders. Further analysis was then done to extract document delivery usage data on unfilled orders. Subject analysis of the collection and usage were done by Library of Congress (LC) Classification and by user group. The data on unfilled document delivery orders identified titles not held at CISTI but from which clients were requesting articles. Data from the National Library of Medicine (NLM) was used to identify titles needed by Canadian libraries but not widely available in Canada. The findings showed that while CISTI clients were well served in most fields, there was a need to enhance the collection, notably in the health sciences and to a lesser extent, business. As a result, 135 new medical and health serial titles were purchased. The study confirmed that document delivery usage data could be used effectively to support strategic library collection development decisions.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.045 | 0.049 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".