External and Internal Citation Analyses Can Provide Insight into Serial/Monograph Ratios when Refining Collection Development Strategies in Selected STEM Disciplines
Notice bibliographique
Résumé
A Review of: Kelly, M. (2015). Citation patterns of engineering, statistics, and computer science researchers: An internal and external citation analysis across multiple engineering subfields. College and Research Libraries, 76(7), 859-882. http://doi.org/10.5860/crl.76.7.859 Objective – To determine internal and external citation analysis methods and their potential applicability to the refinement of collection development strategies at both the institutional and cross-institutional levels for selected science, technology, engineering, and mathematics (STEM) subfields. Design – Multidimensional citation analysis; specifically, analysis of citations from 1) key scholarly journals in selected STEM subfields (external analysis) compared to those from 2) local doctoral dissertations in similar subfields (internal analysis). Setting – Medium-sized, STEM-dominant public research university in the United States of America. Subjects – Two citation datasets: 1) 14,149 external citations from16 journals (i.e., 2 journals per subfield; citations from 2012 volumes) representing bioengineering, civil engineering, computer science (CS), electrical engineering, environmental engineering, operations research, statistics (STAT), and systems engineering; and 2) 8,494 internal citations from 99 doctoral dissertations (18-22 per subfield) published between 2008-–2012 from CS, electrical and computer engineering (ECE), and applied information technology (AIT) and published between 2005-–2012 for systems engineering and operations research (SEOR) and STAT. Methods – Citations, including titles and publication dates, were harvested from source materials and stored in Excel and then manually categorized according to format (book, book chapter, journal, conference proceeding, website, and several others). To analyze citations, percentages of occurrence by subfield were calculated for variables including format, age (years since date cited), journal distribution, and the frequency at which a journal was cited. Top journals for selected subfields were identified based on the percentages of authors citing them in each dataset and, for interdisciplinary journals, according to how often citations for them appeared in subfield groups. Main Results – For each subfield group, distinct patterns emerged for both internal and external analysis in terms of format, currency, and preferred journals. Regarding format of material cited, journals were dominant for external citations and ranged between 40% of citations (CS) to 94% (bioengineering) of formats cited. Formats were more distributed for internal citations, with ECE, SEOR, and STAT exhibiting journal dominance (61%, 30%, and 59% of citations, respectively) and conference proceedings dominant in CS (43%) and AIT (30%). Regarding currency, almost all cited items (>98% for external citations and 96% for internal citations) were published within the last 50 years, with electrical engineering showing the highest percentage of materials cited within the past five years for external citations (47%). For internal citations, applied information technology illustrated the most use of materials in the five-year timeframe (46%). Top journals for each subfield in which only external data were analyzed include Journal of Biomechanics (bioengineering 54%), Engineering Structures (civil engineering 47%), Water Research (environmental engineering 60%). For CS and AIT, the top journal was Communications of the ACM (external CS citations 29%; internal CS 32%; internal AIT 36%). For electrical engineering, the top journals were Electronics Letters (21% external citations) and Proceedings of the IEEE (50% internal citations). SEOR was broken into three categories (systems engineering, SEOR, and operations research), with Systems Engineering being the top journal according to external citations for the subfield of the same name (48%) and Air Traffic Control Quality as the leading SEOR journal (25% internal citations only). Management Science (77% external citations only) was the top journal for operations research. Top STAT journals were Annals of Statistics (96% internal citations) and Journal of the American Statistical Association (60%). Science was the top interdisciplinary journal for external citations (10%) and IEEE: Transactions on Pattern Analysis and Machine Intelligence for internal citations (13%). Conclusion – An approach to citation analysis integrating both internal and external components is useful for institutions aiming to develop balanced STEM collections as well as for collection assessment and budgeting purposes and enables adjustment of serial/monograph ratios to create custom local serial/monograph ratio “blends.” In this institution’s case, internal data suggested a 59:41 serial/monograph ratios versus an external data ratio of 75:25, which indicated that a blended ratio of 67:33 might be appropriate for this institution based on an average of both ratios. In the future, cross-institutional collaboration for external analyses would make it easier for institutions to focus on internal analyses in order to develop appropriate local serial/monograph ratio blends.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,199 | 0,528 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,103 | 0,107 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,018 | 0,020 |
| Science ouverte | 0,004 | 0,009 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,005 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».