Increased Size of E-Book Collection Positively Impacts Usage but May Reach Critical Mass
Bibliographic record
Abstract
Objective – To investigate the impact of collection size, student population, and faculty population on the use of an e-book collection. Design – Longitudinal quantitative analysis. Setting – Mid-sized public university located in Ontario, Canada. Subjects – Data from 79,821 e-books related to searches and viewings; data regarding number of e-books held, students enrolled, and faculty employed at institution. Methods – Numbers of e-books purchased individually and in packages were calculated, followed by the acquisition of annual student and faculty numbers through the University Institutional Planning Office. Searches for and viewings of e-books conducted via vendor websites were obtained directly from vendors. Data for all variables represent years 2002-2010. Main Results – Very high Pearson’s correlation coefficients of r = 0.96 for searches performed and r = 0.91 for viewings were found in relation to the number of e-books held. While the annual increase in number of viewings was at a rate similar to that of e-books available, a 7% decrease in searches and viewings occurred in 2010. In terms of user populations, doctoral students exhibited the strongest association with e-book collection size followed by undergraduate students and faculty. Conclusions – Based upon examination of correlation coefficients, the study concludes that the e-book collection size is closely associated with the level of e-book usage. The author notes that the data suggests use of the collection may possibly have leveled off, implying that additional large increases in the e-book collection could incur unnecessary expenditure. “Viewings per e-book” and “searches per e-book” ratios were highest when e-books were obtained on an individual title-by-title basis, though the author cautions that this does not necessarily prove that selective purchasing results in increased use. A deeper quantitative analysis into e-book usage and academic program size is considered for future research, as well as a comparison between electronic reference books and monographs. The author recommends that similar research be performed at other institutions of varying size to determine whether the study’s results would be replicated.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".