Analysis From Seven Years of DDA-Centered Collections Strategy Indicates Long-Term Effectiveness for Acquiring Electronic Monographs
Bibliographic record
Abstract
A Review of: Lowry, L., Arthur, M. A., & Gilstrap, D. L. (2024). A retrospective look at a DDA-centered collection strategy: Planning for the future of monograph acquisitions. The Journal of Academic Librarianship, 50(1), 102831. https://doi.org/10.1016/j.acalib.2023.102831 Objective – To examine long-term data for confirmation that the Demand-Driven Acquisition (DDA) strategy is a viable method for supporting ebook monograph collections. Design – Analysis of cost, usage data, and Library of Congress Classification (LCC) for DDA monographs. Setting – University of Alabama, a public R1 university. Subjects – Seven years of usage, cost, and classification data for ebook monographs in the DDA pool. Methods – Authors requested data on ebook monographs from EBSCO dating back to the beginning of the DDA plan. After data cleaning, they used Excel PivotTables and PivotCharts for analysis, as well as SPSS linear regression for determining the strength of relationships between key data variables. Main Results – Cost per use of ebooks purchased or loaned through the DDA pool showed a high return on investment. Breaking data down by LCC for regression analysis showed links between the percentage of the DDA pool size and the percentage of “triggered” purchases or loans, as well as between the percentage of full-text requests and the percentage of triggers. The percentage of triggers for a given LCC can be predicted by percentage of the DDA pool and percentage of full-text requests. However, primary LCC was not itself a significant predictor. Conclusion – The authors concluded that DDA plans can act as effective long-term collections strategies but also noted that basing a plan on an existing approval profile and continuous assessment of the plan are useful approaches for ensuring a DDA plan’s success. Supplementary strategies may be necessary for developing areas of the collection where needs are not met by the DDA plan, such as purchasing ebook packages and utilizing approval plans. In addition to overall cost-effectiveness, they further recommended DDA plans because these strategies offer an approach to collection building that frees staff to focus substantial time on other initiatives.
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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 | 0.025 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".