Scientometrics Research Paper

View sample Scientometrics Research Paper. Browse other  research paper examples and check the list of research paper topics for more inspiration. If you need a religion research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our research paper writing service for professional assistance. We offer high-quality assignments for reasonable rates.

1. Introduction

Scientometrics can be defined as the study of the quantitative aspects of scientific communication, R&D practices, and science and technology (S&T) policies. The objective is to develop indicators of the intellectual and social organization or the sciences using network relations between scientific authors and texts. The specialty has developed in relation to the increased capacities of computer storage and information retrieval of scientific communications (e.g., citation analysis). Archival records of scientific communications contain institutional address information, substantive messages (e.g., title words), and relational information from which one is able to reconstruct patterns and identify the latent characteristics of both authors and document sets. Using scientometric techniques, one is thus able to relate institutional characteristics at the level of research groups with developments at the level of scientific disciplines and specialties.

Citations, for example, can be used for retrieving documents on the basis of author names, or vice versa. The scientometric representation is formal: it remains in need of an interpretation. The focus on uncertainty contained in the distribution relates scientometrics additionally to the (neo-evolutionary) study of complex and adaptive systems. Simulation models are increasingly used for the study of the role of sciencebased technologies in innovation processes. However, the specialty remains data driven because of its mission to provide indicators to S&T policy processes and R&D management.

2. A Metric Of Science?

In 1978, the journal Scientometrics was launched as a new medium to stimulate the quantitative study of scientific communication. Derek de Solla Price, one of the founding fathers of the specialty, proclaimed in his introduction the development of scientometrics as the emergence of a ‘relatively hard’ social science. This claim has generated discussion from the very beginning of the specialty. In that same year (1978), leading sociologists of science published an edited volume entitled Toward a Metric of Science: The Advent of Science Indicators, dedicated to ‘Paul F. Lazarsfeld (1901–76), Master of Quantitative and Qualitative Social Research’ (Elkana et al. 1978).

The systematic comparison of science indicators across fields of science was made possible by the creation of the Science Citation Index by Eugene Garfield of the Institute of Scientific Information (Garfield 1979). A preliminary version of this index became available in 1962. The creation of the database has stimulated the development of new perspectives on studies in various traditions. For example, the growth of scientific disciplines and specialties can be discussed in quantitative terms using this database (e.g., Price 1963), ‘invisible colleges’ can be explained in terms of network structures (Crane 1969), and theories of citations can perhaps be developed and tested (cf. Braun 1998).

The development of the specialty went hand in hand with the need for a means of legitimating science policies (Wouters 1999). Narin (1976) elaborated an instrumentarium for the systematic development of the biennial series of Science Indicators which the National Science Foundation of the USA began providing in 1972. (In 1987, the name of this series was changed to Science and Engineering Indicators.) With the further development of S&T policies in other nation-states and with the gradual emergence of such policies at the European level, scientometrics became a booming business during the 1980s. By comparing radio-astronomy facilities at the international level, Martin and Irvine (1983) showed the feasibility of comparing research groups in terms of quantitative performance indicators.

In a special issue of Social Studies of Science about performance indicators, Collins (1985) raised the question of the unit of analysis: what is being assessed in terms of what? The authors, the papers, or the cognitions shaped in terms of sociocognitive interactions among authors and discourses? In relation to French traditions of linguistic analysis, Callon et al. (1983) proposed using words and their co-occurrences instead of citations and co-citations (Small 1973) as units of analysis. Citations can be considered as subtextual codifications, while words indicate the variation at the level of texts. Words may change both in meaning and in terms of their observable frequency distributions.

3. Methodologies

The availability of well-organized relational databases on an annual basis challenges the scientometrician to develop comparative statistics. How is the structure in each of these years to be depicted, and how may changes in structure be distinguished from and related to changes in the observable variation? Can the difference over time be identified as ‘growth of science,’ or is it mainly a difference between two measurement errors? How significant are differences when tested against simulation results?

In principle, the idea of a dynamic mapping of science requires an independent operationalization of structural (that is, latent) dimensions of the maps and observable variation which is to be pencilled into these maps. Science, however, develops not only in terms of the variation, but also by changing its structural dimensions. Because of the prevailing reflexivity within the science system, previous structures can be felt as constraints and at the same time be used as resources. The construction of structure may historically be stabilized, but reflexive actors are able to deconstruct and to assess the previous constructions with hindsight.

The methodological apparatus for the mapping of science in terms of multivariate statistics (multidimensional scaling, cluster analysis, etc.) has been a product of the 1980s (e.g., Van Raan 1988). The 1990s have provided the evolutionary turn: what does history mean in relation to (envisaged?) future options? How can the system itself be informed reflexively with respect to its self-organizing capacities (Leydesdorff 1995)? The relation to technometrics and the measurement of ‘systems of innovation’ has become central to a shifting research agenda. The development of the sciences has increasingly been contextualized in relation to science-based technologies and innovation systems (Gibbons et al. 1994).

3.1 Comparative Statistics Of Science

Various methods for the mapping of science can be based on relational indicators such as citations, words, co-occurrences of each of these categories, etc. Clustering, however, requires the choice of a similarity criterion and a clustering algorithm. The distinction between positional (or factor analytical) and relational (or graph analytical) approaches is also relevant: the network is expected to contain an architecture in which actors have a position. A nearby position does not necessarily imply a relation. The methodological reflection may thus help to clarify the theoretical analysis (Burt 1982).

The mapping requires a specific perspective for the projection. Each perspective assumes a position or a window. If the multidimensional space is highly structured, the different positions may provide nearly incommensurable projections. In an article on the development of the relation between scientometrics and other subfields of S&T studies, Leydesdorff and Van den Besselaar (1997) showed that the mappings depicting science studies from the perspective of the journals Scientometrics, Social Studies of Science, and Research Policy, respectively, are increasingly different during the 1990s. Citation relations among these core journals tend to decrease. The authors characterize Social Studies of Science as the ‘codifier’ of the field (along the historical axis), Scientometrics as the ‘formalizer,’ and Research Policy as the ‘utilizer.’ Only a few scholars in ‘Science, Technology, and Innovation Studies’ have developed competences for communicating across these subdisciplinary boundaries.

3.2 Time-Series Methodologies

During the 1980s a debate raged in the community concerning the scientometric indication of a ‘decline of British science.’ Eventually, a special issue of Scientometrics was devoted to the debate in 1991 (Martin 1991). Some agreement could be reached that the inclusion and exclusion of data types and the framework for the comparison can be crucial for the dynamic evaluation. Should one compare with reference to a previous stage (for example, in terms of an ex ante fixed journal set), or should one with hindsight reconstruct the relevant data? For example, not only has the number of biotechnology journals changed, but also our understanding of ‘biotechnology’ is changing continuously. The sociological understanding of scientific knowledge production and control seems to have eroded Price’s dream of developing scientometrics as a relatively ‘hard social science.’

Using time-series analysis, one is always able to increase the fit of the curve by allowing for higherorder polynomials. Here again, the theoretical appreciation has to guide the choices of the parameters. For example, if one wishes to measure growth, it may be useful to include a second or third-order polynomial in addition to the linear fit. Figure 1, for example, depicts the emergence of the modern citation itself as a historical phenomenon, but using theoretically informed helplines. Scientific citation emerged around the turn of the twentieth century as a means to search in both the textual and the social dimensions of science. Thus, the citation can be considered as an indicator of the complexity of sociocognitive interaction in science after its institutionalization in the nineteenth century.

Scientometrics Research Paper Figure 1

3.3 Neo-Evolutionary Methodologies

The networks of texts and the networks of authors operate upon each other in a selective mode. The distributions are expected to contain information, and this information may be increasingly codified by recurrent selections. As the systems ‘lock-in’ (in terms of their mutual information), the closure of the communication into a paradigm is one among various possibilities. The uncertainties which prevail in these networks can interactively generate codifications, which can be expected to perform a ‘life-cycle.’ Both participants and observers are able to hypothesize these structures reflexively, and new information can be expected to induce an update. Thus, the codification drives the knowledge production process. Codification also provides instruments for local control of otherwise global developments.

The relational operation is recursive. For example, citations refer to other texts and/or to citations in other texts. The networks resulting from this operation are expected to have an architecture (which can be mapped at each moment in time). Operations are expected to be reproduced if they are able to further the production of new knowledge and the latter’s retention into cognitive structures. What is functional, however, is decided at a next moment in time, that is, at the level of a reflexive hypernetwork that overlays the historically generated ones. The distributions indicate the patterns of the expected operations. Thus, the process of new knowledge claims is propelled and made more precise and selective in tradeoffs of references in social and cognitive dimensions.

Scientometric studies can be helpful in revealing the patterns of intellectual and social organization which may have remained ( partially) latent to the knowledgable actors involved. Simulation studies using scientometric mappings as input enable us to indicate the difference that the moves of the players can make. The complexity of the scientists’ worlds is reflected in the scientometric reconstructions. The recognition of these objectified reconstructions recursively assumes and potentially refines the cognition within the discourses at both levels.

Over time, the cognitive reconstruction becomes thoroughly selective: citations may be ‘obliterated by incorporation’ into the body of knowledge, and social factors may play a role in further selections, e.g., in terms of reputations. In this co-evolution between communications and authors, distributions of citations function, among other things, as contested boundaries between specialties. Since the indicators are distributed, the boundaries remain to be validated. Functions are expected to change when the research front moves further. By using references, authors position their knowledge claims within one specialty area or another. Some selections are chosen for stabilization, for example, when codification into citation classics occurs. Some stabilizations are selected for globalization at a next-order level, for example, when the knowledge component is integrated into a technology.

4. Conclusion

The focus on evolutionary dynamics relates scientometrics increasingly with the further development of evolutionary economics (Leydesdorff and Van den Besselaar 1994). How can systems of innovation be delineated? How can the complex dynamics of such systems be understood? How is the ( potentially random) variation guided by previously codified expectations? How can explorative variation be increased in otherwise ‘locked-in’ trajectories of technological regimes or paradigms?

From this perspective, the indication of newness may become more important than the indication of codification. The Internet, of course, offers a research tool for what has also now been called ‘sitations’ (Rousseau 1997). ‘Webometrics’ may develop as a further extension of scientometrics relating this field with other subspecialities of science and technology studies, such as the public understanding of science or the appropriation of technology and innovation using patent statistics.

Bibliography:

  1. Braun T (ed.) 1998 Topical discussion issue on theories of citation. Scientometrics 43: 3–148
  2. Burt R S 1982 Toward a Structural Theory of Action. Academic Press, New York
  3. Callon M, Courtial J-P, Turner W A, Bauin S 1983 From translations to problematic networks: an introduction to coword analysis. Social Science Information 22: 191–235
  4. Collins H M 1985 The possibilities of science policy. Social Studies of Science 15: 554–8
  5. Crane D 1969 Social structure in a group of scientists. American Sociological Review 36: 335–352
  6. Elkana Y, Lederberg J, Merton R K, Thackray A, Zuckerman H 1978 Toward a Metric of Science: The Ad ent of Science Indicators. Wiley, New York
  7. Garfield E 1979 Citation Indexing. Wiley, New York
  8. Gibbons M, Limoges C, Nowotny H, Schwartzman S, Scott P, Trow M 1994 The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies. Sage, London
  9. Leydesdorff L, Van den Besselaar P (eds.) 1994 Evolutionary Economics and Chaos Theory: New Directions in Technology Studies. Pinter, London
  10. Leydesdorff L, Van den Besselaar P 1997 Scientometrics and communication theory: Towards theoretically informed indicators. Scientometrics 38: 155–74
  11. Leydesdorff L, Wouters P 1999 Between texts and contexts: Advances in theories of citation? Scientometrics 44: 169–182
  12. Leydesdorff L A 1995 The Challenge of Scientometrics: The Development, Measurement, and Self-organization of Scientific Communications. DSWO Press, Leiden University, Leiden, Netherlands
  13. Martin B R 1991 The bibliometric assessment of UK scientific performance. A reply to Braun, Glanzel and Schubert. Scientometrics 20: 333–57
  14. Martin B R, Irvine J 1983 Assessing basic research: Some partial indicators of scientific progress in radio astronomy. Research Policy 12: 61–90
  15. Narin F 1976 Evaluative Bibliometrics. Computer Horizons Inc., Cherry Hill, NJ
  16. Price D, de Solla 1963 Little Science, Big Science. Columbia University Press, New York
  17. Raan A F J Van (ed.) 1988 Handbook of Quantitative Studies of Science and Technology. Elsevier, North-Holland, Amsterdam
  18. Rousseau R 1997 Sitations: An exploratory study. Cybermetrics 1: 1 https://www.academia.edu/49519297/Sitations_an_exploratory_study
  19. Small H 1973 Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science 24: 265–9
  20. Wouters P 1999 The citation culture. PhD Thesis, University of Amsterdam
Second Language Acquisition Research Paper
History Of Scientific Revolution Research Paper

ORDER HIGH QUALITY CUSTOM PAPER


Always on-time

Plagiarism-Free

100% Confidentiality
Special offer! Get discount 10% for the first order. Promo code: cd1a428655