How does the Market Use Citation Data? The Hirsch Index in Economics

A large literature following Hirsch (2005) has proposed citation-based indexes that could be used to rank academics. This paper examines how well several such indexes match labor market outcomes using data on the citation records of young tenured economists at 25 U.S. departments. Variants of Hirsch’s index that emphasize smaller numbers of highly-cited papers perform better than Hirsch’s original index and have substantial power to explain which economists are tenured at which departments. Adjustment factors for differences across fields and years of experience are presented.Hirsch index, citation data, economics profession

Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical Review

An exploratory social network analysis of academic research networks

For several decades, academics around the world have been collaborating with the view to support the development of their research domain. Having said that, the majority of scientific and technological policies try to encourage the creation of strong inter-related research groups in order to improve the efficiency of research outcomes and subsequently research funding allocation. In this paper, we attempt to highlight and thus, to demonstrate how these collaborative networks are developing in practice. To achieve this, we have developed an automated tool for extracting data about joint article publications and analyzing them from the perspective of social network analysis. In this case study, we have limited data from works published in 2010 by England academic and research institutions. The outcomes of this work can help policy makers in realising the current status of research collaborative networks in England

The Interdependence of Scientists in the Era of Team Science: An Exploratory Study Using Temporal Network Analysis

How is the rise in team science and the emergence of the research group as the fundamental unit of organization of science affecting scientists’ opportunities to collaborate? Are the majority of scientists becoming dependent on a select subset of their peers to organize the intergroup collaborations that are becoming the norm in science? This dissertation set out to explore the evolving nature of scientists’ interdependence in team-based research environments. The research was motivated by the desire to reconcile emerging views on the organization of scientific collaboration with the theoretical and methodological tendencies to think about and study scientists as autonomous actors who negotiate collaboration in a dyadic manner. Complex Adaptive Social Systems served as the framework for understanding the dynamics involved in the formation of collaborative relationships. Temporal network analysis at the mesoscopic level was used to study the collaboration dynamics of a specific research community, in this case the genomic research community emerging around GenBank, the international nucleotide sequence databank. The investigation into the dynamics of the mesoscopic layer of a scientific collaboration networked revealed the following—(1) there is a prominent half-life to collaborative relationships; (2) the half-life can be used to construct weighted decay networks for extracting the group structure influencing collaboration; (3) scientists across all levels of status are becoming increasingly interdependent, with the qualification that interdependence is highly asymmetrical, and (4) the group structure is increasingly influential on the collaborative interactions of scientists. The results from this study advance theoretical and empirical understanding of scientific collaboration in team-based research environments and methodological approaches to studying temporal networks at the mesoscopic level. The findings also have implications for policy researchers interested in the career cycles of scientists and the maintenance and building of scientific capacity in research areas of national interest

Statistical regularities in the rank-citation profile of scientists

Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile ci(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each ci(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different ci(r) profiles, our results demonstrate the utility of the βi scaling parameter in conjunction with hi for quantifying individual publication impact. We show that the total number of citations Ci tallied from a scientist's Ni papers scales as . Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress

A review of the literature on citation impact indicators

Citation impact indicators nowadays play an important role in research evaluation, and consequently these indicators have received a lot of attention in the bibliometric and scientometric literature. This paper provides an in-depth review of the literature on citation impact indicators. First, an overview is given of the literature on bibliographic databases that can be used to calculate citation impact indicators (Web of Science, Scopus, and Google Scholar). Next, selected topics in the literature on citation impact indicators are reviewed in detail. The first topic is the selection of publications and citations to be included in the calculation of citation impact indicators. The second topic is the normalization of citation impact indicators, in particular normalization for field differences. Counting methods for dealing with co-authored publications are the third topic, and citation impact indicators for journals are the last topic. The paper concludes by offering some recommendations for future research

Theoretical studies of the historical development of the accounting discipline: a review and evidence

Many existing studies of the development of accounting thought have either been atheoretical or have adopted Kuhn's model of scientific growth. The limitations of this 35-year-old model are discussed. Four different general neo-Kuhnian models of scholarly knowledge development are reviewed and compared with reference to an analytical matrix. The models are found to be mutually consistent, with each focusing on a different aspect of development. A composite model is proposed. Based on a hand-crafted database, author co-citation analysis is used to map empirically the entire literature structure of the accounting discipline during two consecutive time periods, 1972–81 and 1982–90. The changing structure of the accounting literature is interpreted using the proposed composite model of scholarly knowledge development