Authors : James Bradley, Sitaram Devarakonda, Avon Davey, Dmitriy Korobskiy, Siyu Liu, Djamil Lakhdar-Hamina, Tandy Warnow, George Chacko
Citation analysis of the scientific literature has been used to study and define disciplinary boundaries, to trace the dissemination of knowledge, and to estimate impact. Co-citation, the frequency with which pairs of publications are cited, provides insight into how documents relate to each other and across fields.
Co-citation analysis has been used to characterize combinations of prior work as conventional or innovative and to derive features of highly cited publications. Given the organization of science into disciplines, a key question is the sensitivity of such analyses to frame of reference.
Our study examines this question using semantically themed citation networks. We observe that trends reported to be true across the scientific literature do not hold for focused citation networks, and we conclude that inferring novelty using co-citation analysis and random graph models benefits from disciplinary context.
DOI : https://doi.org/10.1162/qss_a_00007
Authors : Alberto Baccini, Lucio Barabesi, Mahdi Khelfaoui, Yves Gingras
This paper explores, by using suitable quantitative techniques, to what extent the intellectual proximity among scholarly journals is also proximity in terms of social communities gathered around the journals.
Three fields are considered: statistics, economics and information and library sciences. Co-citation networks represent intellectual proximity among journals. The academic communities around the journals are represented by considering the networks of journals generated by authors writing in more than one journal (interlocking authorship: IA), and the networks generated by scholars sitting on the editorial board of more than one journal (interlocking editorship: IE).
Dissimilarity matrices are considered to compare the whole structure of the networks. The CC, IE, and IA networks appear to be correlated for the three fields. The strongest correlation is between CC and IA for the three fields.
Lower and similar correlations are obtained for CC and IE, and for IE and IA. The CC, IE, and IA networks are then partitioned in communities. Information and library sciences is the field in which communities are more easily detectable, whereas the most difficult field is economics.
The degrees of association among the detected communities show that they are not independent. For all the fields, the strongest association is between CC and IA networks; the minimum level of association is between IE and CC.
Overall, these results indicate that intellectual proximity is also proximity among authors and among editors of the journals. Thus, the three maps of editorial power, intellectual proximity, and authors communities tell similar stories.
DOI : https://doi.org/10.1162/qss_a_00006
Authors : Lovenoor Aulck, Kishore Vasan, Jevin West
Collaborations are an integral part of scientific research and publishing. In the past, access to large-scale corpora has limited the ways in which questions about collaborations could be investigated. However, with improvements in data/metadata quality and access, it is possible to explore the idea of research collaboration in ways beyond the traditional definition of multiple authorship.
In this paper, we examine scientific works through three different lenses of collaboration: across multiple authors, multiple institutions, and multiple departments. We believe this to be a first look at multiple departmental collaborations as we employ extensive data curation to disambiguate authors’ departmental affiliations for nearly 70,000 scientific papers.
We then compare citation metrics across the different definitions of collaboration and find that papers defined as being collaborative were more frequently cited than their non-collaborative counterparts, regardless of the definition of collaboration used.
We also share preliminary results from examining the relationship between co-citation and co-authorship by analyzing the extent to which similar fields (as determined by co-citation) are collaborating on works (as determined by co-authorship).
These preliminary results reveal trends of compartmentalization with respect to intra-institutional collaboration and show promise in being expanded.
URL : https://arxiv.org/abs/1809.04093
Authors : George A. Lozano, Vincent Lariviere, Yves Gingras
Historically, papers have been physically bound to the journal in which they were published but in the electronic age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal’s physical availability, reputation, or Impact Factor.
We compare the strength of the relationship between journals’ Impact Factors and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers’ citation rates were increasingly linked to their respective journals’ Impact Factors.
However, since 1990, the advent of the digital age, the strength of the relation between Impact Factors and paper citations has been decreasing. This decrease began sooner in physics, a field that was quicker to make the transition into the electronic domain.
Furthermore, since 1990, the proportion of highly cited papers coming from highly cited journals has been decreasing, and accordingly, the proportion of highly cited papers not coming from highly cited journals has also been increasing.
Should this pattern continue, it might bring an end to the use of the Impact Factor as a way to evaluate the quality of journals, papers and researchers.”
URL : http://arxiv.org/abs/1205.4328
Réseaux de co-citations et Open Access : pour un renouveau des méthodes d’évaluation :
“Depuis quelques années, la méthodologie en matière d’évaluation des publications scientifiques fait l’objet d’une réflexion accrue, tant de la part des chercheurs que des professionnels de la documentation appelés à l’appliquer. Le modèle classique, en usage dans la majorité des instances administratives, émanant de l’Institute for Scientific Information de Philadelphie (ISI) révèle un certain nombre de limites notamment dues à l’absence de nuances et à des modes de calcul bruts qui semblent privilégier l’aspect comptable et quantitatif au détriment de la qualité. Les modèles alternatifs proposent l’application fine et nuancée de la méthode mathématique de la ” marche aléatoire ” (random walk) avec l’exécution itérative de l’algorithme de Lawrence Page (utilisé par Google pour le tri et l’affichage des pages Web) algorithme pondéré connu sous l’appellation Weighted PageRank. Associée à la détection des co-citations, cette méthode permet de visualiser des collèges invisibles, des affinités entre titres de périodiques et surtout la traçabilité des citations. Celle-ci est déterminante dans la définition qualitative de l’évaluation qui semble minorée dans le modèle classique.”
URL : http://archivesic.ccsd.cnrs.fr/sic_00589630/fr/
Interlinking journal and wiki publications through joint citation: Working examples from ZooKeys and Plazi on Species-ID :
“Scholarly publishing and citation practices have developed largely in the absence of versioned documents. The digital age requires new practices to combine the old and the new. We describe how the original published source and a versioned wiki page based on it can be reconciled and combined into a single citation reference. We illustrate the citation mechanism by way of practical examples focusing on journal and wiki publishing of taxon treatments. Specifically, we discuss mechanisms for permanent cross-linking between the static original publication and the dynamic, versioned wiki, as well as for automated export of journal content to the wiki, to reduce the workload on authors, for combining the journal and the wiki citation and for integrating it with the attribution of wiki contributors.”
URL : http://www.pensoft.net/journals/zookeys/article/1369/abstract/interlinking-journal-and-wiki-publications-through-joint-citation
Author Co-Citation Analysis (ACA): a powerful tool for representing implicit knowledge of scholar knowledge workers :
“In the last decade, knowledge has emerged as one of the most important and valuable organizational assets. Gradually this importance caused to emergence of new discipline entitled ―knowledge management‖. However one of the major challenges of knowledge management is conversion implicit or tacit knowledge to explicit knowledge. Thus Making knowledge visible so that it can be better accessed, discussed, valued or generally managed is a long-standing objective in knowledge management. Accordingly in this paper author co- citation analysis (ACA) will be proposed as an efficient technique of knowledge visualization in academia (Scholar knowledge workers).”
URL : http://eprints.rclis.org/handle/10760/15501