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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 OpenAlex

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
venuePublié dans une revue dont le pays d'attache est le Canada.

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.

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,778
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0020,426
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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