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Enregistrement W2561312713 · doi:10.18438/b8933q

National Differences in Perceived Benefit of Libraries May Be Due to Their Investments in Libraries, Library Supply, and Cultural Factors

2016· article· en· W2561312713 sur OpenAlexvenueno aff
Ann Glusker

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2016
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Administration
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRespondentDescriptive statisticsDocumentationSample (material)PsychologyPopulationDemographyLibrary scienceMedical educationGeographySociologyStatisticsMedicinePolitical scienceMathematicsComputer science

Résumé

récupéré en direct d'OpenAlex

A Review of: Vakkari, P., Aabø, S., Audunson, R., Huysmans, F, Kwon, N., Oomes, M., & Sin, S. (2016). Patterns of perceived public library outcomes in five countries. Journal of Documentation, 72(2), 342–361. http://dx.doi:org/10.1108/JD-08-2015-0103 Objective – To compare citizens' perceptions of the benefits of libraries in five culturally diverse countries. Design – Postal survey to a random stratified sample and web surveys (some with a sampling plan, some apparently not). Setting – Surveys were administered in Finland (by post), Norway, the Netherlands, the United States of America, and South Korea (online). Subjects – Selected or self-selected members of the general adult population in the specified countries who had used a public library within the past year. Methods – Surveys were administered and data were collected in each of the five countries. A dependent variable representing perceived outcomes was calculated from 19 outcome measures (related to life experiences). Within this, 4 indices were calculated from subsets of the 19 measures, relating to work, education, everyday activities, and leisure activities. Five independent variables were used: frequency of library use, number of services used, gender, age, and education level. Respondent country was also entered into analyses. Descriptive statistics and analysis of covariance results were presented. Main Results – It was noted that each country's sample was skewed in some way towards one or more of the variables of gender, age, and education, and some statistical corrections were employed. While patterns within countries are similar, library users from Finland, the United States of America, and South Korea reported higher levels of benefits overall. "Fun in reading" and "self-education" were the two outcomes with the highest scores by respondents. Higher numbers of visits and greater use of services may account for the higher perceived benefits in the three countries reporting them. In fact, these two factors appear to explain a substantial portion of the variance in perceptions of benefits between countries, meaning that between-country variation in library resources and supply plays a role in perception of benefit. There were varied rather than linear patterns of benefit reporting along age and education continua, with those at the lowest education levels deriving the most perceived benefits in all spheres. By gender, women derived fewer perceived benefits in the work sphere than men. Conclusions – There is variation across countries in the level of public library benefits reported, as well as variation across individual measures, creating different profiles of response by country. Even when respondent demographic characteristics and library usage are controlled for, country differences remain. These may be explained by the differences in investment in – and hence supply of – libraries by country, types of investment (e.g., according to the authors, Finland invests in services, Norway in collections, and the USA in staffing), and cultural factors such as the propensity of USA respondents to have a more extreme response style. Future research may profitably concentrate on policy contexts of libraries in each country. In the nineteenth century libraries provided social welfare services and in the twentieth they provided human rights through equitable access to information, so research should focus, by country, on what libraries will provide in the twenty-first century. Future studies might also address how differences in demographic patterns among respondents play out in benefit perceptions between countries.

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 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)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
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 enseignants0,631
Score d'incertitude au seuil0,873

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
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 savante0,0010,403
Science ouverte0,0000,000
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 Opus0,042
Tête enseignante GPT0,283
Écart entre enseignants0,240 · 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 tête enseignante, pas un consensus.

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
Publié2016
Routes d'admission1
Résumé présentoui

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