Toward a Formula-Based Model for Academic Library Funding: Statistical Significance and Implications of a Model Based upon Institutional Characteristics
Bibliographic record
Abstract
This study tests the hypothesis that a positive relationship exists between academic library funding (dependent variable) and selected institutional variables taken as indicators of the demand for library services at the university (enrollment, number of doctoral programs, doctoral degrees awarded, number of faculty, select other institutional characteristics). The research employs 11 years of longitudinal data from 113 members of the Association of Research Libraries to create a multiple regression model. Empirical results indicate that operational indicators of the demand for library services are positively associated with funding, and most of the associations are statistically significant at the five percent level or less in two tail tests. In a corollary finding, libraries associated with private universities in the United States spend 21 percent more than their public counterparts, while Canadian university libraries spend 21 percent less than U.S. public university libraries. The presence of a medical school is associated with an 8.6 percent greater expenditure, and the presence of a law school is associated with a 12.3 percent greater expenditure. The study suggests that this formula may be useful as a tool for library funding and assessment of adequacy of library budgets.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".