There is a Relationship between Resource Expenditures and Reference Transactions in Academic Libraries
Bibliographic record
Abstract
A Review of: Dubnjakovic, A. (2012). Electronic resource expenditure and the decline in reference transaction statistics in academic libraries. Journal of Academic Librarianship, 38(2), 94-100. doi: 10.1016/j.acalib.2012.01.001 Objective – To provide an analysis of the impact of expenditures on electronic resources and gate counts on the increase or decrease in reference transactions. Design – Analysis of results of existing survey data from the National Center for Educational Statistics (NCES) 2006 Academic Library Survey (ALS). Setting – Academic libraries in the United States. Subjects – 3925 academic library respondents. Methods – The author chose to use survey data collected from the 2006 ALS conducted by the NCES. The survey included data on various topics related to academic libraries, but in the case of this study, the author chose to analyze three of the 193 variables included. The three variables: electronic books expenditure, computer hardware and software, and expenditures on bibliographic utilities, were combined into one variable called electronic resource expenditure. Gate counts were also considered as a variable. Electronic resource expenditure was also split as a variable into three groups: low, medium, and high. Multiple regression analysis and general linear modeling, along with tests of reliability, were employed. Main Results – The author determined that low, medium, and high spenders with regard to electronic resources exhibited differences in gate counts, and gate counts have an effect on reference transactions in any given week. Gate counts tend to not have much of an effect on reference transactions for the higher spenders, and higher spenders tend to have a higher number of reference transactions overall. Low spenders have lower gate counts and also a lower amount of reference transactions. Conclusion – The findings from this study show that academic libraries spending more on electronic resources also tend to have an increase with regard to reference transactions. The author also concludes that library spaces are no longer the determining factor with regard to number of reference transactions. Spending more on electronic resources is also important to increase both in-person and electronic reference transactions.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".