Comparing usage between a Dynamic and a Static e-monograph Collection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it