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Enregistrement W2298886116 · doi:10.18438/b8dc9p

Open Access Complements Interlibrary Loan Services, but Additional User Education is Needed

2016· article· en· W2298886116 sur OpenAlexaffvenue
Richard Hayman

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2016
Typearticle
Langueen
DomaineComputer Science
ThématiqueLibrary Collection Development and Digital Resources
Établissements canadiensMount Royal University
Organismes subventionnairesnon disponible
Mots-clésInterlibrary loanComputer scienceShared resourceService (business)World Wide WebResource (disambiguation)Plug-inBusinessComputer securityOperating system

Résumé

récupéré en direct d'OpenAlex

A Review of: Baich, T. (2015). Open access: Help or hindrance to resource sharing? Interlending & Document Supply, 43(2), 68-75. http://dx.doi.org/10.1108/ILDS-01-2015-0003 Abstract Objective – To examine interlibrary loan (ILL) request rates for open access (OA) materials and determine how OA may affect resource sharing. This research updates the author’s previous study. Design – Quantitative analysis. Setting – A large, urban, public research university library system in the United States of America. Subjects – 1,557 open access ILL material requests among 23,531 total ILL requests submitted during the 2012 and 2013 fiscal years (July 2011-June 2013). Methods – The library has tracked and recorded OA requests that appear among ILL material requests since 2009. Using OCLC’s ILLiad software to manage ILL requests, they have implemented two custom routines. One routine is for open access searching on standard items, and uses software plugins to search across various open resources. All materials published prior to 1923 are treated as being in the public domain, so requests for these materials are automatically routed to this queue. The second custom routine is used for searching for OA electronic theses and dissertations, and is employed when the requested resource is not found in the library’s subscription resources. Other article requests are routed to the RapidILL service for open access availability. Main Results – The research presented reveals that ILL requests for OA materials exhibited a steady increase year over year, while overall ILL requests decreased slightly. This finding is true both for the fiscal years reported in this study and also the years since the author’s original study in 2011 (Baich, 2012). Of the 1,557 OA requests examined, 72% (n=1,135) were for journal articles, 8% (n=125) were for books or book chapters, 9% (n=140) were for theses or dissertations, 3% (n=54) were for conference papers, and 7% (n=105) were for reports. Library staff typically fill these article requests using gold OA or green OA sources. The researcher notes the difficulty in refining by source, though confirmed that 15% of articles requested (n=170) were filled using a gold OA source, and that another 30 article requests (~2.6%) were filled with materials available in the public domain. This leads to the conclusion that the majority of article requests are filled using green OA sources. As the library also includes OA collections within its electronic resources, staff filled 13% of ILL article requests (n=152) using journals and repositories from these sources. Another 16% of article requests were filled using a combination of various online open repositories, including subject repositories (n=83), institutional repositories (n=84), or national or consortial repositories (n=16). The author includes a similar breakdown of fulfillment rates and sources for the other main categories explored – books and book chapters, theses and dissertations, conference papers, and reports – representing a combined 27% of all OA ILL requests. Regarding this content, it is noteworthy that overall open access requests for these material categories has dropped across each category when compared to the author’s previous study, with the exception of report requests, which more than doubled compared to that previous study. The study includes a brief overview of the user status for users making the various open access requests, with undergraduate students (n=283) and graduate students (n=807) combined making 70% of all requests. Subject areas are also briefly examined, with ILL requests coming from 63 different schools or departments across the library system. The top 15 are reported, with Psychology being the top requester (n=198), followed closely by Engineering & Technology (n=182). The author notes that 7 of the top 15 are STEM or health science disciplines. Conclusion – The rate of ILL requests for OA materials shows that library users continue to struggle with information retrieval. The researcher concludes that in many cases, making an ILL request is easier for the user than completing a thorough search. Since staff resources are being redirected to fill user requests for materials that are readily available through open access, this use of staff time may have impacts on resource sharing and the library’s ability to fill ILL requests. The author identifies benefits of using OA resources, including an increased ability of staff to fulfill ILL requests, especially when providing grey literature, theses and dissertations, and conference papers and reports. Another identified benefit was the decreased turnaround time for securing materials, with immediate availability via OA saving 1.15 days to deliver materials to the user. Finally, the library estimates cost savings of over $27,000 (USD), based on estimated traditional per unit ILL costs.

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)0,021
score de la tête « metaresearch » (Gemma)0,074
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante, Science ouverte
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 enseignants0,997
Score d'incertitude au seuil0,983

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0210,074
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,010
Études des sciences et des technologies0,0040,005
Communication savante0,0160,037
Science ouverte0,0030,017
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,2940,142

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 Opus0,028
Tête enseignante GPT0,297
Écart entre enseignants0,269 · 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.

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

Citations2
Publié2016
Routes d'admission2
Résumé présentoui

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