Using document delivery data for selecting medical titles in a large STM library: the experience of CISTI
Bibliographic record
Abstract
Purpose The Canada Institute for Scientific and Technical Information (CISTI) undertook an in‐depth analysis of its current serial subscriptions to determine whether they were meeting the needs of internal clients at the National Research Council of Canada (NRC) and document delivery clients. The assumptions were that extended gaps existed in business literature needed by NRC clients and medical literature needed by document delivery clients. Seeks to address this issue. Design/methodology/approach The analysis was done from two perspectives: review and analysis of usage of the print serials subscriptions; and analysis of unfilled document delivery orders. The project team matched current serial titles with document delivery usage and then classified the titles by subject. Second, the team used data from unfilled orders to create a ranked list of titles not held at CISTI but for which clients were requesting articles. The ranked titles were validated by data from the National Library of Medicine (NLM) on titles requested by Canadian libraries and not widely available in Canada. Findings NRC users showed a need for more business titles and all client groups showed a marked need for medical titles. While 36 percent of titles in the collection were medical, they accounted for 57.2 percent of document delivery activity and for 64.6 percent of unfilled orders. As a result, CISTI purchased 135 new medical serial subscriptions and will update its collection development policy to allow for a broader collection in medicine and business. Originality/value The study shows that document delivery usage data can play a key role in supporting strategic collection decisions.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this metaresearch. It is in the settled core of the field.
Library and information science study using document delivery and serials usage data to guide collection development at Canada's national science library; the object is how a research library serves researchers.
It studies information-use and collection-development practices in the Canadian scientific research library system.
LIS collection-development study using document-delivery data at CISTI, a Canadian STM research library.
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 | 0.043 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.028 | 0.049 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".