Bibliographic record
Abstract
A Review of: Leykam, Andrew. “Exploring Interlibrary Loan Usage Patterns and Liaison Activities: The Experience at a U.S. University.” Interlending & Document Supply 36.4 (2008): 218-24. Objective - To investigate Interlibrary Loan (ILL) usage patterns, and connect them to liaison activities beyond collection development. Design – Pattern analysis of ILL requests. Setting – Library of The College of Staten Island, a mid-size, public university with predominantly undergraduate enrolment. Subjects – 4,875 identifiable requests over a three-year period. Methods – A data set of requests for ILLs of monographs over a period of three years was acquired from OCLC resource sharing statistics. This data was manually reviewed to remove duplicate records of the same request, but not multiple requests for the same item. The data included requestor status, department, publication date and subject classification of requested items. Main Results – Differences in use across user statuses and departments were identified. Conclusion – Usage Patterns can accurately illustrate trends in the borrowing behaviour of patrons, and be used to inform liaison librarians about user needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".