Comparison of Print Monograph Acquisitions Strategies Finds Circulation Advantage to Firm Orders
Bibliographic record
Abstract
A Review of: Ke, I., Gao, W., & Bronicki, J. (2017). Does title-by-title selection make a difference? A usage title analysis on print monograph purchasing. Collection Management, 42(1), 34-47. http://dx.doi.org/10.1080/01462679.2016.1249040 Abstract Objective – To compare usage of print monographs acquired through firm order to those acquired through approval plans. Design – Quantitative study. Setting – A public research university serving an annual enrollment of over 43,500 students and employing more than 2,600 faculty members in the South Central United States. Subjects – Circulation and call number data from 21,356 print books acquired through approval plans, and 23,920 print books acquired through firm orders. Methods – Item records for print materials purchased between January 1, 2011 and December 31, 2014 were extracted from the catalog and separated by acquisitions strategy into firm order and approval plan lists. Items without call numbers and materials that had been placed on course reserves were removed from the lists. The authors examined accumulated circulation counts and conducted trend analyses to examine year-to-year usage. The authors also measured circulation performance in each Library of Congress call number class; they grouped these classes into science, social science, and humanities titles. Main Results – The authors found that 31% of approval plan books and 39% of firm order books had circulated at least once. The firm order books that had circulated were used an average of 1.87 times, compared to approval plan books which were used an average of 1.47 times. The year-to-year analysis showed that the initial circulation rate for approval plan books decreased from 42% in 2011 to 14% in 2014, and from 46% to 24% for firm order books. Subject area analysis showed that medicine and military science had the highest circulation rates at over 45%, and that agriculture and bibliography titles had the lowest circulation rates. Subject area groups showed the same pattern, with books in the social sciences and sciences experiencing more significant circulation benefits to firm order purchasing. Conclusion – Monographs acquired through firm orders circulated at a slightly higher rate than those acquired through approval plans.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".