First-Time Use Books are Frequently Available for Patron-Driven Acquisition
Bibliographic record
Abstract
A Review of: Herrera, G. (2015). Testing the patron-driven model: Availability analysis of first-time use books. Collection Management, 40(1), 3-16. doi: http://dx.doi.org/10.1080/01462679.2014.965863 Abstract Objective – To determine whether a hypothetical Patron-Driven Acquisition (PDA) purchasing model is acceptable in terms of making available print monographs after their initial publication. Design – Quantitative data analysis. Setting – A large public university located in the southern United States of America. Subjects – 8,020 item records representing books used at the author’s institution for the first time in 2012. Non-circulating monographs and items such as personal copy reserve materials and government documents were excluded from the sample. Methods – Using the libraries’ ILS, a listing of the titles of monographs that received first-time use in 2012 was generated and exported to Microsoft Excel. The Getting It System Toolkit (GIST) was used to batch-search possibilities for acquisition and/or access, including purchase (including Amazon and Better World Books) and free access (such as HaithiTrust and Google Books). Main Results – A total of 76% (6,130) of titles from the sample of 8,020 were available for purchase. A total of 3% (165) of these titles were both available for purchase and freely available online. Books not available either freely or by purchase represented 21% (1,682) of the sample. When participation in a regional resource-sharing consortium was accounted for, only 1% (101) of the titles could not be obtained. Books published before the 1920s were more likely to be freely available due to being in the public domain; however a majority of the titles (64%; 5,127) had a publication date of 1990 forward. The humanities represented the largest disciplinary grouping at 57% (4,563), with Social Sciences (31%; 2,472) and STEM (11%; 879) following. Conclusions – In sum, the results indicated a very low margin of unavailability for titles. The author notes that, based on the findings, there should be no PDA purchase restrictions according to publication date if a large-scale program were to be implemented at their institution, and that researchers requiring humanities titles would be likely to benefit most from such a program (p. 14). It should be noted that a significant budget for PDA was allocated at the author’s institution.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.218 | 0.065 |
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".