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Enregistrement W1485734132 · doi:10.18438/b82p6j

Patron-Driven Acquisition of E-Books Satisfies Users’ Needs While Also Building the Library’s Collection

2013· article· en· W1485734132 sur OpenAlexaffvenue
Giovanna Badia

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2013
Typearticle
Langueen
DomaineComputer Science
ThématiqueLibrary Collection Development and Digital Resources
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésLibrary scienceSelection (genetic algorithm)Computer scienceCollection developmentLibrary catalogWorld Wide WebSchool libraryArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

Objective – To present the initial results of an academic library’s one-year pilot with patron-driven acquisition of e-books, which was undertaken “to observe how user preferences and the availability of e-books interacted with [the library’s] traditional selection program” (p. 469). Design – Case study. Setting – The University of Iowa, a major urban research university in the United States. Subjects – Original selection of 19,000 e-book titles from ebrary at the beginning of the pilot in October 2009. To curb spending during the pilot, the number of e-book titles available for purchase was reduced to 12,000 titles at the end of December 2009, and increased to nearly 13,000 titles in April 2010. Methods – These e-book titles were loaded into the library’s catalogue. The goal was for the University of Iowa’s faculty, staff, and students to search the library catalogue, discover these e-book titles, and purchase these books unknowingly by accessing them. The tenth click by a user on any of the pages of an e-book caused the title to be automatically purchased for the library (i.e., ebrary charged the library for the e-book). Main Results – From October 2009 to September 2010, the library acquired 850 e-books for almost $90,000 through patron-driven acquisition. The average amount spent per week was $1,848 and the average cost per book was $106. Researchers found that 80% of the e-books purchased by library patrons were used between 2 to 10 times in a 1-year period. E-books were purchased in all subject areas, but titles in medicine (133 titles purchased, 16%), sociology (72 titles purchased, 8%), economics (58 titles purchased, 7%), and education (54 titles purchased, 6%) were the most popular. Two of the top three most heavily used titles were standardized test preparation workbooks. In addition, 166 of the e-books purchased had print duplicates in the library, and the total number of times the print copies circulated dropped 70% after the e-versions of these books were obtained. The authors also examined usage data for their subscription to ebrary’s Academic Complete collection from September 2009 to July 2010, which consisted of 47,367 e-books. Together with the 12,947 book titles loaded into the catalogue for the patron-acquisition pilot, there were a grand total of 60,314 ebrary e-book titles in the library catalogue that were accessible to the Iowa University community. The study revealed that 15% of these titles were used during this 11-month period, and the used titles were consulted 3 or more times. The authors sorted the user sessions by publisher and found that patrons used e-books from a wide variety of publishing houses, of which numerous university presses together constituted the majority of uses. The five most heavily used e-books were in the fields of medicine, followed by economics, sociology, English-American literature, and education. Conclusion – The authors’ experience has shown that patron-driven acquisition “can be a useful and effective tool for meeting user needs and building the local collection” (p. 490). Incomplete coverage of academic publications makes patron-driven acquisition only one tool among others, such as selection by liaison librarians, which may be employed for collection development. According to the authors, patron-driven acquisition “does a good job of satisfying the sometimes unrecognized demand for interdisciplinary materials often overlooked through traditional selection methods,” (p. 491) and alerts librarians to new research areas.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,029
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,060

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,029
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0030,001
Communication savante0,0070,007
Science ouverte0,0020,005
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0180,008

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,209
Écart entre enseignants0,197 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2013
Routes d'admission2
Résumé présentoui

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