scholarly journals Modeling the structure of recent philosophy

Synthese ◽  
2019 ◽  
Author(s):  
Maximilian Noichl
Keyword(s):  
Dimensionality Reduction ◽  
Unsupervised Learning ◽  
Web Of Science ◽  
Continental Philosophy ◽  
Citation Data ◽  
Disciplinary Structure ◽  
Citation Database ◽  
Recent Philosophy ◽  
Large Corpus

Abstract This paper presents an approach of unsupervised learning of clusters from a citation database, and applies it to a large corpus of articles in philosophy to give an account of the structure of the discipline. Following a list of journals from the PhilPapers-archive, 68,152 records were downloaded from the Reuters Web of Science-Database. Their citation data was processed using dimensionality reduction and clustering. The resulting clusters were identified, and the results are graphically represented. They suggest that the division of analytic and Continental philosophy in the considered timespan is overstated; that analytical, in contrast to Continental philosophy does not form a coherent group in recent philosophy; and that metaphors about the disciplinary structure should focus on the coherence and interconnectedness of a multitude of smaller and larger subfields.

scholarly journals Journals Supporting Terrorism Research: Identification and Investigation into Their Impact on the Social Sciences

2013 ◽  
Vol 74 (2) ◽  
pp. 119-130 ◽  
Author(s):  
Daryl R. Bullis ◽  
Richard D. Irving
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Political Violence ◽  
Web Of Science ◽  
Citation Data ◽  
Journal Literature ◽  
Social Science Journal ◽  
Subject Categories ◽  
The Social ◽  
The Impact

A citation analysis of two preeminent terrorism journals (Terrorism and Political Violence and Studies in Conflict and Terrorism) was used to identify 37 additional social science journals of significant importance to terrorism research. Citation data extracted from the Web of Science database was used to investigate the impact of the two journals on the social science journal literature. The impact of the two journals was also analyzed in terms of SSCI subject categories. This study could provide useful information for collection development librarians interested in the social sciences.

scholarly journals CiteWiz: A Tool for the Visualization of Scientific Citation Networks

2007 ◽  
Vol 6 (3) ◽  
pp. 215-232 ◽  
Author(s):  
Niklas Elmqvist ◽  
Philippas Tsigas
Keyword(s):  
User Study ◽  
Citation Networks ◽  
Graph Layout ◽  
Citation Data ◽  
Citation Database ◽  
Basic Force ◽  
Scientific Citation ◽  
The Individual ◽  
High Level ◽  
Level Analysis

We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent–child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data.

scholarly journals Tracking self-citations in academic publishing

2019 ◽  
Author(s):  
Ameni Kacem ◽  
Justin W. Flatt ◽  
Philipp Mayr
Keyword(s):  
Web Of Science ◽  
Research Evaluation ◽  
Academic Disciplines ◽  
Academic Publishing ◽  
Citation Data ◽  
Playing Field ◽  
Accurate Picture ◽  
Subject Areas ◽  
Problematic Area ◽  
Citation Metrics

AbstractCitation metrics have value because they aim to make scientific assessment a level playing field, but urgent transparency-based adjustments are necessary to ensure that measurements yield the most accurate picture of impact and excellence. One problematic area is the handling of self-citations, which are either excluded or inappropriately accounted for when using bibliometric indicators for research evaluation. Here, in favor of openly tracking self-citations we report on self-referencing behavior among various academic disciplines as captured by the curated Clarivate Analytics Web of Science database. Specifically, we examined the behavior of 385,616 authors grouped into 15 subject areas like Biology, Chemistry, Science & Technology, Engineering, and Physics. These authors have published 3,240,973 papers that have accumulated 90,806,462 citations, roughly five percent of which are self-citations. Up until now, very little is known about the buildup of self-citations at the author-level and in field-specific contexts. Our view is that hiding self-citation data is indefensible and needlessly confuses any attempts to understand the bibliometric impact of one’s work. Instead we urge academics to embrace visibility of citation data in a community of peers, which relies on nuance and openness rather than curated scorekeeping.

scholarly journals Unsupervised Learning with Contrastive Latent Variable Models

2019 ◽  
Vol 33 ◽  
pp. 4862-4869 ◽  
Author(s):  
Kristen A. Severson ◽  
Soumya Ghosh ◽  
Kenney Ng
Keyword(s):  
Dimensionality Reduction ◽  
Unsupervised Learning ◽  
Probabilistic Model ◽  
Latent Variable ◽  
Prior Information ◽  
Data Exploration ◽  
Subgroup Discovery ◽  
Latent Space ◽  
Set Membership ◽  
Data Points

In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.

scholarly journals E-Journal Metrics for Collection Management: Exploring Disciplinary Usage Differences in Scopus and Web of Science

2016 ◽  
Vol 11 (2) ◽  
pp. 97 ◽  
Author(s):  
Katherine Chew ◽  
Mary Schoenborn ◽  
James Stemper ◽  
Caroline Lilyard
Keyword(s):  
Web Of Science ◽  
Journal Article ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Research Impact ◽  
Health Sciences ◽  
Citation Data ◽  
Patterns Of Use ◽  
Thomson Reuters ◽  
Local Institution

Objective – The purpose was to determine whether a relationship exists between journal downloads and either faculty authoring venue or citations to these faculty, or whether a relationship exists between journal rankings and local authoring venues or citations. A related purpose was to determine if any such relationship varied between or within disciplines. A final purpose was to determine if specific tools for ranking journals or indexing authorship and citation were demonstrably better than alternatives. Methods – Multiple years of journal usage, ranking, and citation data for twelve disciplines were combined in Excel, and the strength of relationships were determined using rank correlation coefficients. Results – The results illustrated marked disciplinary variation as to the degree that faculty decisions to download a journal article can be used as a proxy to predict which journals they will publish in or which journals will cite faculty’s work. While journal access requests show moderate to strong relationships with the journals in which faculty publish, as well as journals whose articles cite local faculty, the data suggest that Scopus may be the better resource to find such information for these journals in the health sciences and Web of Science may be the better resource for all other disciplines analyzed. The same can be said for the ability of external ranking mechanisms to predict faculty publishing behaviours. Eigenfactor is more predictive for both authoring and citing-by-others across most of the representative disciplines in the social sciences as well as the physical and natural sciences. With the health sciences, no clear pattern emerges. Conclusion – Collecting and correlating authorship and citation data allows patterns of use to emerge, resulting in a more accurate picture of use activity than the commonly used cost-per-use method. To find the best information on authoring activity by local faculty for subscribed journals, use Scopus. To find the best information on citing activity by faculty peers for subscribed titles use Thomson Reuters’ customized Local Journal Use Reports (LJUR), or limit a Web of Science search to local institution. The Eigenfactor and SNIP journal quality metrics results can better inform selection decisions, and are publicly available. Given the trend toward more centralized collection development, it is still critical to obtain liaison input no matter what datasets are used for decision making. This evidence of value can be used to defend any local library “tax” that academic departments pay as well as promote services to help faculty demonstrate their research impact.

Scientific Grey Literature in a Digital Age: Tracking its Use and Influence in an Evolving Information Economy

2013 ◽  
Author(s):  
Gregory R. G. Hutton
Keyword(s):  
Environmental Protection ◽  
Web Of Science ◽  
Grey Literature ◽  
Digital Age ◽  
Citation Data ◽  
Information Economy ◽  
Marine Environmental Protection ◽  
Digital Formats ◽  
Marine Environmental ◽  
Joint Group

This paper outlines methodologies to improve understanding of the influence of grey literature published in print and digital formats. The study is based on analyses of citation data regarding the UN-based Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection collected from Web of Science, Google, and Google Scholar.Cet article propose des méthodologies pour améliorer notre compréhension de l'influence de la littérature grise publiée sous forme imprimée ou numérique. L'étude est fondée sur des analyses de données de citation concernant le groupe d'experts conjoint de l'ONU chargé d'étudier les aspects scientifiques de la protection du milieu marin recueillies dans Web of Science, Google et Google Scholar. 

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