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Enregistrement W2097026038 · doi:10.18438/b89c70

Looking for Links: How Faculty Research Productivity Correlates with Library Investment and Why Electronic Library Materials Matter Most

2015· article· en· W2097026038 sur OpenAlexvenueno aff
Michael Rawls

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2015
Typearticle
Langueen
DomaineDecision Sciences
Thématiquescientometrics and bibliometrics research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOperationalizationProductivityInvestment (military)InstitutionVariety (cybernetics)Scholarly communicationOrdinary least squaresPublic relationsComputer scienceMarketingSociologyBusinessEconomicsPolitical scienceSocial scienceEconometricsPublishing

Résumé

récupéré en direct d'OpenAlex

Abstract Objective – This paper summarizes two studies that share the same research question: do universities produce more scholarly research when they invest more in their libraries? Research libraries spend a great deal of effort reporting their expenditures, collections statistics, and other measures that serve as a basis for interlibrary comparison and even rankings. The straightforward assumption implied by this activity is that libraries better serve their student and research communities when they are well-funded and well-resourced. The studies examined here both ask if that notion can be validated empirically, not because research libraries require some sort of justification, but because in an environment of tough budget decisions and shifting opinions about the changing role of libraries, it may be useful to demonstrate that sustained investment in libraries offers tangible returns or that the failure to do so can result in tangible costs. Methods – A cross-sectional design featuring ordinary least squares regression analysis was used in both studies to estimate the relationship between scholarly research productivity at U.S. doctoral institutions and an array of institutional characteristics presumed to influence that productivity. The concept of research productivity is operationalized as the total number of scholarly journal articles produced by each institution over a five year period – as journal articles represent the most common form of scholarly expression across the greatest number of academic fields. Serving as the dependent variable, this data was regressed against a variety of institutional characteristics including faculty size, research expenditures, and grant awards, and several library variables centered mostly on expenditures. The concept behind this design is that to realistically explore the relationship between levels of library investment and research productivity, all other institutional drivers of research productivity must also be represented in the dataset. While the design was similar for both studies, they each drew on different data sources and marginally different populations. Results – Both studies found that an institution’s research productivity is positively and significantly correlated with the level of investment it makes in its libraries. Furthermore, both studies found electronic library material expenditures to be particularly associated with increased productivity. This relationship was so strong that an institution’s level of research productivity appears to be sensitive to how its library’s collection budget is allocated between print and electronic materials. As the portion of the budget dedicated to non-electronic material grew, research productivity decreased in statistically significant fashion in both studies. Conclusion – While both studies succeeded in demonstrating the existence of an empirical relationship between library investment and research productivity, the most intriguing finding is that both studies observed a decrease in number of journal articles being produced as expenditures for non-electronic library materials increased. The conclusion is that the efficiencies of electronic resources offer such advantages over the use of traditional library materials in supporting scholarly research that productivity suffers as institutions dedicate a greater portion of their collection budgets to print materials at the expense of electronic materials.

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)0,005
score de la tête « metaresearch » (Gemma)0,059
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Bibliométrie
Catégories consensuellesaucune
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,997
Score d'incertitude au seuil0,059

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,059
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,008
Études des sciences et des technologies0,0010,001
Communication savante0,0070,005
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0180,003

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 Opus0,328
Tête enseignante GPT0,474
Écart entre enseignants0,146 · 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
DomaineIncitatifs
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

Citations9
Publié2015
Routes d'admission1
Résumé présentoui

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