Inter-Library Loans and Document Supply Services in Italy Appear to Supplement Journal Subscriptions Rather Than Replace Them
Bibliographic record
Abstract
Objectives – To examine patterns of Inter-Library Loans and Document Supply (ILL) in a large network of libraries over a period of five years, in order to establish whether ILL services are being used to replace journal subscriptions. The authors also aimed to establish which journal titles were most requested in their network, to inform future acquisitions policy. Design – Longitudinal study using transactional data collected from the Italian Network for Inter-Library Document Exchange (NILDE) for the period 2005-2009. Setting – The Italian library sector. Subjects – Member libraries of NILDE, which is the largest ILL network in Italy with several hundred libraries. These consist primarily of university libraries, but also hospitals and health research institutions, public research institutions, and not-for-profit and public organisations. Methods – ILL request data collected from the NILDE network software were analyzed. Figures were retrieved for the number of different journal titles requested per year of the study overall and by individual institution. Further analysis was undertaken on requests for more recent articles, those published up to five years prior to being requested. This involved creating a list of the most requested titles for each year, and then compiling a core collection of journals that were requested 20 or more times in each year of the study. These core titles were analyzed for trends by subject and publisher, and for any significant correlations between either Impact Factors (IFs) or citation counts and ILL requests for particular journals. Main Results – The data revealed that the number of ILLs processed through NILDE increased every year during the period of the study. The majority of journals were only requested a small number of times in the five year period of the study, with 60% being requested five times or less. In the majority of instances, institutions were not borrowing the same title regularly. Analysis of the core collection of journals revealed that these repeated requests of the same title were mainly in the biomedical sciences and science and technology subject areas, and that these journals were often produced by smaller publishers who were not included in consortia purchasing. There was no correlation between journal impact factors (IFs) and ILL requests, but there was a statistically significant correlation between citation counts and ILL requests. Conclusions – ILL numbers are increasing despite big deals and consortia purchasing. The majority of requests are for articles that are two years old or older, and the authors suggest that this indicates that ILLs do not influence journal subscriptions. The authors suggest that ILLs may have increased during the course of the study (and may continue to do so) due to the current global financial crisis and its impact on library acquisitions.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".