- Sergio D'Antonio Maceiras (Universidad Complutense de Madrid) email
- Angel Gordo López (Universidad Complutense) email
- Celia Díaz-Catalán (Universidad Complutense de Madrid) email
Main objective is to explore different collectives participating in STS diffusion to analyze strategies editors and authors lead to cope with metrics pressure. A second purpose is to generate a network where sharing experiences and ideas to produce new actors in the publications debate.
In recent decades, the scientific metrics have been multiplied exponentially thanks to the application of information technologies. The use of impact factor and other more recent related indexes in the allocation funds process has been enacted. However, there have been different initiatives, as DORA Declaration or Leiden Manifesto calling the attention on both, the unintended consequences of the excessive use of research metrics and the misuse of these forms of assessment. Some of them are related with the contempt of local research, mainly in Social Sciences.
On the other hand, knowledge diffusion, one of the first aims in Science, is easier than ever with the ICT use as the creation of countless journals highlights. But these new actors face many decisions everyday: open or closed publication, Green or Gold Open Access; .xml or not; English or own languages; SSCI, SCOPUS, none of them and then remain outside hegemonic direction.
Main objective in this track is to explore different collectives participating in STS diffusion and to analyze strategies both editors and authors lead to cope with metrics pressure without forgetting the knowledge diffusion objective. A second, but not less important purpose is to generate a network where sharing experiences and ideas in these directions to produce new actors in the publications debate.
This track is closed to new paper proposals.
The role and effects of metrics in the scholarly system
In the era of NPM, metrics have entered the science policy landscape. We will show the effects these metrics have, and the pressure this creates on academics to publish in internationally oriented journals, against a background of fundamental changes in the scientific communication system.
With the advent of New Public Management techniques in science policy since the 1990's, the usage of bibliometric indicators supporting decision making in science policy processes at various levels in the scholarly system has increased substantially. Next to all kind of advanced bibliometric methodologies, simple indicators such as the Journal Impact Factor and the h-index have been among the most used bibliometric indicators in processes of hiring and promoting of scholars, journal selection procedures by both scholars and librarians, as well granting and research funding decision making procedures. One of those applications is the usage of bibliometric indicators for performance based research funding mechanisms. In this contribution we will talk about some of those applications, the rationale behind these applications, the effects these applications might have on the scholarly system, and the consequences in a somewhat wider context. This perspective will be confronted with the more recent development towards Open Science/Open Access, and the pressure this builds upon in particular younger scholars in making an academic career, and more specifically for those in the social sciences and humanities. Finally, some of the myths surrounding these bibliometric indicators will be discussed. The contribution will contain empirical data from studies in which metrics were designed to support decision making in funding allocation models.
Scientific communication networks. Analysis of journals and International Bibliographic Indexes.
SCI use as objetive tool to represent science is controversial. Here we assume papers as technoscientific object in the context of ANT. We propose three networks tracking of scientific communication: manifested, underlying, and associative, to rethink both positives and negatives arguments about SCI.
International Bibliographic Indexes (IBIs) like Science Citation Index (SCI), have play an important role in the world of science. However, IBIs increased use has been controversial and it has raised positives and negatives standpoints regarding their use as objective tools to represent and track the state of science. This has led to the emergence of deep tensions between both sides, expressed in oppositions like: IBIs are/are not objective, scientific, and universal. However, it is paradoxical that both sides are presented as mutually exclusive when in reality the complaints of both consider aspects that coexist in the communication process. We present evidence of this coexistence. In our approach research papers are conceived as a techno-scientific object in the context of actor-network theory and metaphors tracking: optical, industrial, and associative, to analyze scientific communication networks. The aim of this study is twofold: first, we propose three networks tracking of scientific communication: manifested, underlying, and associative, to reconsider both positives and negatives allegations about IBIs, and second, we encourage to rethink the opposition mentioned before, starting from a more relational rather than essentialist approach, to the process of scientific communication.
Key words: STS; Scientometrics; Actor-Network; Journals.
Performance-based allocation of funds, pressure to publish and publication strategies
We contribute to the strand of research by systematically investigating which role the performance-based allocation of funds plays for the criteria researchers use to choose journals to which they submit their work. The analysis draws on unique data by the 2016 DZHW Scientists Survey.
Research on factors influencing researchers' publication strategies is relatively scarce. The few studies dealing with this topic emphasize that, nowadays, researchers are actors on quasi-markets and that funds for research are increasingly redistributed to high-performing researchers (e.g., Enders et al. 2015). We draw on this finding and contribute to the strand of research by systematically investigating how the performance-based allocation of funds affects researchers' publication strategies. More precisely, we examine which role the performance-based allocation of funds plays for the criteria researchers use to choose journals to which they submit their work. Doing so, we focus not only on the direct effects the performance-based allocation of funds has on researchers' publication strategies, but also on indirect effects of the performance-based allocation of funds, e.g. mediated by perceived pressure to publish. The analysis draws on unique data by the 2016 DZHW Scientists Survey. The survey is representative of researchers at German universities and comprises all information necessary to deliver a deeper understanding how the performance-based allocation of funds affects researchers' publication strategies. In order to account for subject-specific differences in academic disciplines, multilevel models are estimated.
Scientific knowledge and social impact on Biotechnology
We aim at analyzing how new research areas are born and perceived by society. We use automated data collection and data mining techniques for analyzing scientific and social information (articles and tweets). We find that its crucial to pay more attention to the social perception of scientific research.
The aim of this paper is to study how new research areas born and how these research areas are perceived by society. We construct a methodology, "knowledge genealogies", which is built using diverse automated data collection and data mining techniques for analyzing scientific and social information. We analyze the scientific biotechnology community and how social media perceives it. We choose this field because of its diversity (in research terms, techniques and products) and possible "tensions" between different applications (e.g. genetic engineering for agriculture vs. health applications).
Indicators of scientific production include: the total number of articles published in high impact journals (308,079 articles in 205 journals downloaded from the "Biotechnology and Microbiology Applied" JRC subject category (ISI) from 1997 to 2011) and a subset of keywords of those articles (a total 187,218 for 2011) that allow building clusters and networks. Social information for each theme includes 375,660 tweets containing any keywords of a subset that are classified by sentiment (positive, negative and neutral) for a total of 33,900 tweets for 2012.
We show that biotechnology was an active research field with eight well-defined themes and promising from a social point of view. We show that our method is able to provide social and scientific information across themes and components. The sentiment analysis indicates that the social perception of our subset is perceived mainly in a neutral manner skewed towards a negative influence, indicating that more attention needs to be place towards the social perception of scientific research.
Co-word Maps and Topic Modeling: A Comparison from a User's Perspective
The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the coloring of the nodes according to the results of the topic model provides maps that are difficult to interpret.
Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. We return to the word/document matrix using first a single text with a strong argument ("The Leiden Manifesto") and then upscale to a sample of moderate size (n = 687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the coloring of the nodes according to the results of the topic model provides maps that are difficult to interpret. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping.
This track is closed to new paper proposals.