Research Productivity and Its Relationship to Library Collections
Bibliographic record
Abstract
Objective – The purpose of this study was to explore in the current academic library environment, the relationship between library collections data (collections’ size, expenditures, and usage) and faculty productivity (scholarly output). The researchers also examined the degree to which new and existing library metrics predict faculty productivity. Methods – Demographic data (e.g., faculty size, student size, research and development expenditures), library budget data (e.g., collection expenditures), collection use data (e.g., full-text article requests and database searches), and publication output for 81 doctoral granting universities in the United States were collected to explore potential relationships between research productivity, collection use, library budgets, collection size, and research expenditures using partial correlations. A hierarchical multiple regression was also used to ascertain the significance of certain predictors of research productivity (publications). Results – A correlation existed between the number of publications (research productivity) and library expenditures (total library expenditures, total library material expenditures, and ongoing library resource expenditures), collection size (volumes, titles, and ebooks), use of collection (full-text article requests and total number of references in the articles), and research and development expenditures. Another key finding from the hierarchical multiple regression analysis showed that full-text article requests were the best predictor of research productivity, which uniquely explained 10.2% of the variation in publication. Conclusion – The primary findings were that full-text article requests, followed by library material expenditures and research expenditures, were found to be the best predictor of research productivity as measured by articles published.
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 | 0.012 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".