Comparing usage between a Dynamic and a Static e-monograph Collection
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present the results from a quantitative analysis comparing usage levels between an e-monograph collection that has experienced continual growth and an e-monograph collection that has not experienced any recent growth whatsoever. The aim of the study was to determine quantitatively if e-monograph collections with dynamic content experience greater levels of usage compared to e-monograph collections that are static in both size and content. Design/methodology/approach – E-monograph data were separated into a Dynamic and a Static Collection. Usage for e-monographs belonging to the Dynamic Collection was compared to usage of e-monographs belonging to the Static Collection. The number of e-monographs was obtained by simple count. Additional statistics tracked include the number of viewings. A linear regression analysis was used to determine the strength of the linear relationship between collection size and usage. Findings – Results indicate that e-monograph collections that continue to grow in both size and content also continue to experience year-to-year increases in usage, whereas e-monograph collections that remain static in size and content experience a decline in usage. A linear regression analysis indicates the existence of a very strong linear relationship that exists between Dynamic Collection size and usage. A weaker linear relationship was calculated for Static Collection size and usage. Originality/value – This research is one of very few studies systematically and quantitatively comparing usage levels between e-monographs from growing collections to collections that have not had any new titles added recently.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".