Thank You for Your Suggestion! Analyzing Patron Purchase Requests at the University of Alberta Library
Bibliographic record
Abstract
Objectives – To understand how many of the user recommendations for new library acquisitions come from high-volume requesters, whether requests are submitted for a person’s own use or on behalf of someone else, and to develop understanding of the reasons given for acquisition requests. Additionally, this work sought to understand approaches to “suggest a purchase” forms at comparator institutions. This understanding would support a review of the University of Alberta Library’s approach to soliciting patron purchase requests, including a review of the form used by patrons to submit these requests. Methods – User recommendations for new library acquisitions at the University of Alberta are received through a “suggest a purchase” form. These form submissions populate a centralized request database, and this database was used to create a dataset of requests for review. A total of 4,681 requests received between April 1, 2021, to March 31, 2024, for non-subscription materials were reviewed in detail. Results – This analysis found that 17% of the requests were submitted by 8 individuals who submitted over 50 requests each, with a further 11% submitted by 15 individuals who submitted between 26-50 requests. While half of all requests were submitted by those who indicated that the item was for their own use, high-volume requesters were more likely than low-volume requesters to submit a request on behalf of someone else. The reason provided in about one third of the requests was categorized as “collection development”, meaning that the user suggested that the material would be beneficial to the collection but did not indicate that they themselves would use it. In reviewing “suggest a purchase” forms from comparator institutions, there was a lack of consensus around requested information or intended audience for this service. Conclusion – As 28% of the requests received at the University of Alberta during this three-year timeframe came from 23 individuals, this work demonstrates that the library’s “suggest a purchase” program does not have broad engagement relative to the size of the library’s community. The wide variety of academic library approaches to submission forms suggests that there is not a clear purpose or approach to receiving these requests. Providing this service requires a significant investment in staff time, yet without a clear purpose and limited user engagement it is unlikely that this service is fulfilling its potential and may instead be detracting from institutional diversity, equity, and inclusion goals. However, considering the large proportion of collection development requests, and the fact that high-volume requesters submit forms on behalf of others, this service could be explored as a means of community engagement and collection diversification. At the University of Alberta Library, this analysis supported the implementation of a program called “Broaden Our Bookshelf” as well as changes to the suggestion form to create a more welcoming user experience that would also enhance departmental understanding of user needs and future assessment of the service.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.023 |
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".