Use of ESBCO Discovery Tool at One University Reveals Increased Use of Electronic Collections but Decreased Use in Circulation of Print Collections
Bibliographic record
Abstract
A Review of: Calvert, K. (2015). Maximizing academic library collections: Measuring changes in use patterns owing to EBSCO Discovery Service. College & Research Libraries, 76(1), 81-99. http://dx.doi.org/10.5860/crl.76.1.81 Objective – To find out what the effects of a discovery tool are in relation to usage of print and electronic library collections, and with the aim to measure the effects in three specific areas: circulation numbers, use of electronic resources, and interlibrary loan requests. Design – Comparative quantitative analysis of usage statistics and data sets. Setting – A regional comprehensive university in the United States of America. Subjects – Usage data from a university library. Methods – The methods used were informed by three hypotheses stated at the beginning of the study. First, an analysis of usage data of e-resources tested the hypothesis that the introduction of a discovery tool would increase use of e-resources. Second, to test whether the use of print collections increased, circulation statistics including items borrowed via consortia and in-house use statistics were measured. Finally, interlibrary loan statistics from 2010 to 2013 were collated to test if the EBSCO Discovery Service (EDS) led to a decrease in interlibrary loan requests. Main Results – The introduction of the EBSCO discovery tool resulted in increased use of EBSCOhost and other databases at the library in question. However, the library's circulation statistics decreased, with a drop of 28% of checkouts compared to the previous year. The drop is more pronounced with undergraduates, who checked out 39% fewer items after the EDS was introduced. There was a 30% decrease in requests for borrowing items from a consortia. There was insufficient data to support or refute the third hypothesis. Conclusion – The implementation of a discovery tool at one library has had both postive and negative outcomes. An increase in the use of electronic collections was observed as a positive outcome, whereas a decrease in the use of print collections was a negative outcome. Due to the findings of the study, the library revised its policy on content inclusion to the EDS. Any new content is now screened for suitability before it is included. As a changing student demographic evolves at the library, with an increase in distance and online learners, the library will grow its collection in line with their needs. The author notes that a further study is needed to examine ebook usage, and recommends that the library consider a move towards ebooks for all
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".