Thank You for Your Suggestion! Analyzing Patron Purchase Requests at the University of Alberta Library
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,010 | 0,015 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,023 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».