Bibliographic record
Abstract
Purpose – The purpose of this article was to present the results of a quantitative analysis that compared usage levels between an e-reference collection that has experienced continual updated content and growth and an e-reference collection that has not experienced any recent changes. The aim of the study was to determine quantitatively if e-reference collections with dynamic content experience greater levels of usage compared to e-reference collections that are static in both size and content. Design/methodology/approach – E-reference data were separated into a dynamic collection and a static collection. Usage for e-reference belonging to the dynamic collection was compared to usage of e-reference belonging to the static collection. The number of e-reference 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-reference collections that continue to grow in both size and content also continue to experience year-to-year increases in usage. E-reference collections that remain static in size and content experienced a decline in usage. A linear regression analysis indicates the existence of an extremely strong linear relationship between dynamic content and usage. A weaker linear relationship was calculated for static content. Originality/value – To this author’s knowledge, this research is the first to systematically and quantitatively compare usage levels between e-reference titles from growing collections to collections that have not had any new titles added recently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".