Bibliometric studies on single journals: a review

This paper covers a total of 82 bibliometric studies on single journals (62 studies cover unique titles) published between 1998 and 2008 grouped into the following fields; Arts, Humanities and Social Sciences (12 items); Medical and Health Sciences (19 items); Sciences and Technology (30 items) and Library and Information Sciences (21 items). Under each field the studies are described in accordance to their geographical location in the following order, United Kingdom, United States and Americana, Europe, Asia (India, Africa and Malaysia). For each study, elements described are (a) the journal’s publication characteristics and indexation information; (b) the objectives; (c) the sampling and bibliometric measures used; and (d) the results observed. A list of journal titles studied is appended. The results show that (a)bibliometric studies cover journals in various fields; (b) there are several revisits of some journals which are considered important; (c) Asian and African contributions is high (41.4 of total studies; 43.5 covering unique titles), United States (30.4 of total; 31.0 on unique titles), Europe (18.2 of total and 14.5 on unique titles) and the United Kingdom (10 of total and 11 on unique titles); (d) a high number of bibliometrists are Indians and as such coverage of Indian journals is high (28 of total studies; 30.6 of unique titles); and (e) the quality of the journals and their importance either nationally or internationally are inferred from their indexation status

The metric tide: report of the independent review of the role of metrics in research assessment and management

This report presents the findings and recommendations of the Independent Review of the Role of Metrics in Research Assessment and Management. The review was chaired by Professor James Wilsdon, supported by an independent and multidisciplinary group of experts in scientometrics, research funding, research policy, publishing, university management and administration. This review has gone beyond earlier studies to take a deeper look at potential uses and limitations of research metrics and indicators. It has explored the use of metrics across different disciplines, and assessed their potential contribution to the development of research excellence and impact. It has analysed their role in processes of research assessment, including the next cycle of the Research Excellence Framework (REF). It has considered the changing ways in which universities are using quantitative indicators in their management systems, and the growing power of league tables and rankings. And it has considered the negative or unintended effects of metrics on various aspects of research culture. The report starts by tracing the history of metrics in research management and assessment, in the UK and internationally. It looks at the applicability of metrics within different research cultures, compares the peer review system with metric-based alternatives, and considers what balance might be struck between the two. It charts the development of research management systems within institutions, and examines the effects of the growing use of quantitative indicators on different aspects of research culture, including performance management, equality, diversity, interdisciplinarity, and the ‘gaming’ of assessment systems. The review looks at how different funders are using quantitative indicators, and considers their potential role in research and innovation policy. Finally, it examines the role that metrics played in REF2014, and outlines scenarios for their contribution to future exercises

Changing patterns of self-citation: Cumulative inquiry or self-promotion?

Self-citations are a familiar, if sometimes controversial, element of academic knowledge construction and reputation-building, contributing to both the cumulative nature of academic re-search and helping writers to promote their scientific authority and enhance their careers. As scholarly publications become more specialised, more collaborative and more important for promotion and tenure, we might expect self-citation to play a more visible role in published research and this paper explores this possibility. Here we trace patterns of self-citation in papers from the same five journals in four disciplines at three-time periods over the past 50 years, selected according to their impact ranking in 2015. We identify a large increase in self-citations although this is subject to disciplinary variation and tempered by a huge rise in citations overall, so that self-citation has fallen as a proportion of all citations. We attempt to ac-count for these changes and give a rhetorical explanation for authorial practices

Emergence of computing education as a research discipline

This thesis investigates the changing nature and status of computing education research (CER) over a number of years, specifically addressing the question of whether computing education can legitimately be considered a research discipline. The principal approach to addressing this question is an examination of the published literature in computing education conferences and journals. A classification system was devised for this literature, one goal of the system being to clearly identify some publications as research – once a suitable definition of research was established. When the system is applied to a corpus of publications, it becomes possible to determine the proportion of those publications that are classified as research, and thence to detect trends over time and similarities and differences between publication venues. The classification system has been applied to all of the papers over several years in a number of major computing education conferences and journals. Much of the classification was done by the author alone, and the remainder by a team that he formed in order to assess the inter-rater reliability of the classification system. This classification work led to two subsequent projects, led by Associate Professor Judy Sheard and Professor Lauri Malmi, that devised and applied further classification systems to examine the research approaches and methods used in the work reported in computing education publications. Classification of nearly 2000 publications over ranges of 3-10 years uncovers both strong similarities and distinct differences between publication venues. It also establishes clear evidence of a substantial growth in the proportion of research papers over the years in question. These findings are considered in the light of published perspectives on what constitutes a discipline of research, and lead to a confident assertion that computing education can now rightly be considered a discipline of research

Measuring the Influence and Impact of Competitiveness Research: A Web of Science Approach

The purpose of this paper is to measure the influence and impact of competitiveness research by identifying the 100 most cited articles in competitiveness that are published in academic journals indexed in the database of Web of Science of the Institute for Scientific Information (ISI) between 1980 and 2013. Using citation analysis we investigated the number of citations that were made to the 100 most cited articles that deal with competitiveness during this 34 year period. We also identified articles, authors, journals, institutions, and countries that have had the most contribution to the literature of competitiveness. Further, we determined in which categories of Web of Science these articles were published and how is the time distribution of their publication. Additionally, we investigated the level of competitiveness that has received the most attention, and the latest level of analysis in competitiveness research. We also explored the type of research design these articles used. Finally, we determined the most popular topics covered and the type of firm or industry/ name of nation or region analyzed by these articles. The findings of this research provide a reliable basis for competitiveness researchers to better plan their studies and enhance the influence and impact of their research works. However, the most cited articles published in other databases and categories, and citation to these articles in other publications and resources may deserve future research attention

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas