Interlibrary Loan Rates for Academic Libraries in the United States of America Have Increased Despite the Availability of Electronic Databases, but Fulfilment Rates Have Decreased
Bibliographic record
Abstract
Objectives – To determine the number of interlibrary loan (ILL) requests in academic libraries in the United States of America over the period 1997-2008, and how various factors have influenced these rates. These factors included electronic database subscriptions, size of print journal and monograph collections, and the presence of link resolvers. Data were collected from libraries as both lenders and borrowers. The study also looked at whether the number of professional staff in an ILL department had changed during the period studied, and whether ILL departments led by a professional librarian correlated positively with rates of ILL. Design – Online questionnaire. Setting – Academic library members of the Online Computer Library Center (OCLC) ILL scheme in the United States of America. Subjects – A total of 442 academic library members of the OCLC ILL scheme. Methods – An electronic questionnaire was sent to 1433 academic library member institutions of the OCLC ILL scheme. Data were collected for libraries as both lending and borrowing institutions. Data were analyzed using a statistical software package, specifically to calculate Spearman’s rank correlations between the variables and rates of ILL. Main Results – Responses to the electronic questionnaire were received from 442 (31%) academic libraries. There was an overall increase in the number of ILL requests in the period 1997-2008. The number of ILL requests which were unfulfilled also increased during this period. There was a positive correlation between rates of ILL and all of the variables investigated, with the strongest correlations with size of print monograph collections and size of print journal collections. The numbers of staff in ILL departments remained relatively static during the period covered by the study, although the majority of staff working in ILL was composed of paraprofessionals. There was a weak positive correlation between numbers of ILL requests and whether ILL departments were headed by a professional librarian. Conclusions – Access to full text electronic databases has not decreased the numbers of ILL requests in academic libraries in the United States of America. In fact, ILL requests have increased, probably due to the fact that students and staff of academic libraries now have access to a larger number of citations through online databases and other information sources. The authors suggest that the increase in unfulfilled ILL requests is also due to this increased access. Libraries with large print collections are more likely to receive ILL requests precisely because they have more material to lend out, and may make more ILL requests due to the research output of their presumably larger institutions. There may be a higher number of ILL requests fulfilled by departments headed by a professional librarian because a librarian has more knowledge of sources to fulfil requests.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".