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Enregistrement W1545807730 · doi:10.18438/b8b017

Users’ Awareness of Electronic Books is Limited

2007· article· en· W1545807730 sur OpenAlexvenueno aff
Gale G. Hannigan

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2007
Typearticle
Langueen
DomaineComputer Science
ThématiqueLibrary Collection Development and Digital Resources
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGraduate studentsLibrary scienceMedical educationIncentivePsychologyComputer scienceMedicine

Résumé

récupéré en direct d'OpenAlex

A review of: Levine-Clark, Michael. “Electronic Book Usage: A Survey at the University of Denver.” Portal: Libraries and the Academy 6.3 (Jul. 2006): 285-99. Abstract Objective – To determine if university library users are aware of electronic books, and how and why electronic books are used. Design – Survey. Setting – University of Denver. Subjects – Two thousand sixty-seven graduate and undergraduate students, faculty, and staff. Methods – In Spring 2005, the University of Denver faculty, and graduate and undergraduate students were invited to participate in a survey about awareness and use of electronic books. A link to the survey was also posted on the library’s home page and on the university’s Web portal. The 19-question survey consisted of 11 questions to get feedback about electronic books in general, five questions focused on netLibrary, and the remaining were demographic questions. Eligibility to win one of two university bookstore gift certificates provided incentive to complete the survey. Main results – Surveys were completed by 2,067 respondents, including undergraduate students (30.1%), graduate students (39.1%), faculty (12.5%), and staff (11.8%). Results were reported by question, broken out by status (undergraduate students, graduate students, faculty) and/or by discipline (Business, Humanities, Nontraditional, Professional, Sciences, Social Sciences), and presented in tables or in the text. In general, most respondents (59.1%) were aware that the library provides access to electronic books. The library catalog and professors were the main ways respondents learned about electronic books. Approximately half (51.3%) indicated they had used an electronic book. Of those who indicated that they used electronic books (1,061 respondents), most (72%) had used electronic books more than once. The main reasons mentioned for choosing to use an electronic book included: no print version available, working from home makes getting to the library difficult, and searching text in an electronic book is easier. When asked about typical use of electronic books, most respondents indicated they read only a part of an electronic book; only 7.1% of 1,148 respondents indicated they read the entire electronic book. In answer to a question about choosing the print or electronic version of the same book, 60.7% responded that they would always or usually use print, and 21.5% indicated they would always or usually use electronic. The amount of material to read, the need to refer to the material at a later time, and the desire to annotate or highlight text are all factors that influence whether users read electronic books on a computer or PDA, or print out the material. U.S. government publications and netLibrary were the electronic resources used the most by survey participants. Conclusion – The results of this survey suggest the need to market availability of the library’s electronic books. Problems associated with the use of electronic books are related to reading large amounts of text on a computer screen, but a reported benefit is that searching text in an electronic book is easier. Responses to the survey suggest that the use of electronic resources may not be generic, but rather depends on the type of resource (content) being used. The author notes that this finding should lead to further investigation of which items will be preferred and used in which format.

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)0,001
score de la tête « metaresearch » (Gemma)0,009
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: aucune
Score de désaccord entre enseignants0,098
Score d'incertitude au seuil0,329

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,001
Communication savante0,0040,005
Science ouverte0,0010,002
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0980,022

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 Opus0,015
Tête enseignante GPT0,240
Écart entre enseignants0,225 · 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

Citations4
Publié2007
Routes d'admission1
Résumé présentoui

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