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Enregistrement W2432609838 · doi:10.18438/b8rh0r

Patron Time-Use May Be an Effective Metric for Presenting Library Value to Policy Makers

2016· article· en· W2432609838 sur OpenAlexvenueaboutno aff
Ann Glusker

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2016
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic and Environmental Valuation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMetric (unit)LoanComputer scienceStatisticsOperations managementMathematicsBusinessEngineeringFinance

Résumé

récupéré en direct d'OpenAlex

Objective – To test a metric for library use, that could be comparable to metrics used by competing government departments, for ease of understanding by policy makers.
 
 Design – Four types of data were collected and used: Time-diaries, exit surveys, gate counts, and circulation statistics.
 
 Setting – A large public library in British Columbia, Canada.
 
 Subjects – Time-diary subjects were 445 patrons checking out materials; exit survey subjects were 185 patrons leaving the library.
 
 Methods – A paper-based time diary, prototypes of which were tested, was given to patrons who checked out library materials during a one-week period. These patrons were charged with recording the use of the checked-out items during the entire three-week loan period. From this information, the average number of hours spent with various types of loaned material (print and audio/DVD) was calculated. The average number of hours spent per item type was then applied to the circulation statistics for those items, across a month, to get a total of hours spent using all circulated material during that month. During the same one-week period of time-diary distribution, exit surveys were conducted by library staff with patrons leaving the library, asking them how long they had spent in the library during their current visit. The average number of minutes per visit was calculated and then applied to the gate count for the month, to get a total number of minutes/hours spent “resident” in the library that month. Adding the totals, a grand total of patron time-use hours was calculated. A monetary value was applied per hour, using the results of a contingent valuation study from Missoula, Montana (Dalenberg et al., 2004), in order to convert hours of library benefit into a dollar figure.
 
 Main Results – There was a 24% response rate for the time diaries (106/445). The diary entries yielded an average of 3.5 hours of time-use per print item, and 1.9 hours per DVD. The range for audio materials was quite wide, and for all item types, a few heavy users skewed the averages. Hours of secondary use (when people other than the original borrower read, listened to, or watched, the materials) were calculated, and represented 13% of the total hours. The average amount of time spent per visit was 42 minutes. Applying these averages to one month of circulation figures and gate counts, respectively, the result was that patrons spent 182,000 hours using library services in one month. Applying dollar amounts of benefit per hours spent, based on the Missoula study, the result was that patrons had received $842,000 of benefit from their use of the library in that month.
 
 Conclusions – This study confirmed that the prototype performance measure of hours of patron use, and refinements in obtaining it, was a useful tool with which to present the case for the value of libraries to policy makers. The study estimates that 90% of library use occurs off-site, and that a surprising proportion of that use is by secondary users. Future studies could refine the collection methodologies even more by collecting demographic information, by mapping user activities during branch visits, and by obtaining better information about secondary users of materials. Future research should also take into account: seasonal effects on borrowing, reading level of borrowers, and possibilities for collecting information in online formats. With these developments, it might be possible to assign “enjoyment levels” to items in library catalogs.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,838
Score d'incertitude au seuil0,775

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,236
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,041
Tête enseignante GPT0,249
Écart entre enseignants0,208 · 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'étudeThéorique ou conceptuel
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'admission2
Résumé présentoui

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