Looking for Links: How Faculty Research Productivity Correlates with Library Investment and Why Electronic Library Materials Matter Most
Bibliographic record
Abstract
Abstract Objective – This paper summarizes two studies that share the same research question: do universities produce more scholarly research when they invest more in their libraries? Research libraries spend a great deal of effort reporting their expenditures, collections statistics, and other measures that serve as a basis for interlibrary comparison and even rankings. The straightforward assumption implied by this activity is that libraries better serve their student and research communities when they are well-funded and well-resourced. The studies examined here both ask if that notion can be validated empirically, not because research libraries require some sort of justification, but because in an environment of tough budget decisions and shifting opinions about the changing role of libraries, it may be useful to demonstrate that sustained investment in libraries offers tangible returns or that the failure to do so can result in tangible costs. Methods – A cross-sectional design featuring ordinary least squares regression analysis was used in both studies to estimate the relationship between scholarly research productivity at U.S. doctoral institutions and an array of institutional characteristics presumed to influence that productivity. The concept of research productivity is operationalized as the total number of scholarly journal articles produced by each institution over a five year period – as journal articles represent the most common form of scholarly expression across the greatest number of academic fields. Serving as the dependent variable, this data was regressed against a variety of institutional characteristics including faculty size, research expenditures, and grant awards, and several library variables centered mostly on expenditures. The concept behind this design is that to realistically explore the relationship between levels of library investment and research productivity, all other institutional drivers of research productivity must also be represented in the dataset. While the design was similar for both studies, they each drew on different data sources and marginally different populations. Results – Both studies found that an institution’s research productivity is positively and significantly correlated with the level of investment it makes in its libraries. Furthermore, both studies found electronic library material expenditures to be particularly associated with increased productivity. This relationship was so strong that an institution’s level of research productivity appears to be sensitive to how its library’s collection budget is allocated between print and electronic materials. As the portion of the budget dedicated to non-electronic material grew, research productivity decreased in statistically significant fashion in both studies. Conclusion – While both studies succeeded in demonstrating the existence of an empirical relationship between library investment and research productivity, the most intriguing finding is that both studies observed a decrease in number of journal articles being produced as expenditures for non-electronic library materials increased. The conclusion is that the efficiencies of electronic resources offer such advantages over the use of traditional library materials in supporting scholarly research that productivity suffers as institutions dedicate a greater portion of their collection budgets to print materials at the expense of electronic materials.
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 | 0.005 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".