BIBLIOMETRICS

The question of which aspect of the communication process and discourse formation within science is to be considered as an object for study is a complex one. Choosing one aspect of scientific communication such as the formal communication through scientific publications may be seen as short-sighted in view of the great amount of other formal (books, monographs, project reports, etc.) and informal (verbal exchange, grey literature, etc.) communication taking place in scientific communities. However, to carry out an analysis on both of these levels is an immense task, and so the field must be narrowed down to units that are manageable to analyze. Formal scientific communication, as is the case of scientific articles in peer-reviewed journals, is chosen as unit of analysis because the straightforwardness of its essence: knowledge is textually expressed, presented to peers for reviews, accepted and brought to the whole of the scientific community through its publication; without which, it is difficult for knowledge to be accepted as such by the entire community (Felt et al., 1995: pp.66-67). Each scientist with something to say (who has created knowledge) can create formal texts (articles) which are later submitted for their inclusion and discussion within the community through peer-review processes, gaining then the necessary legitimation for the created knowledge.

To study this particular form of science communication, a method was needed which helped determine how to select the scientific publications to be considered as well as help evaluate these objects in view of answering the particular question of scientific collaboration. Bibliometrics is a set of methods used to study or measure texts and information, it has now-a-days frequently been coupled with the measuring of scientific productivity, as these tools have been proven to mirror the intellectual influences of actual scientific work (Borgman, 2000: p.145). Tools of bibliometrics are, for example, citation frequencies: which record the number of citations a scientific article has gathered throughout time and, journal impact factor: which records the frequency with which the

“average article” in a journal has been cited in a given period of time, usually one or two years after its publication date (Moed and Leeuwen, 1995: p.461; van Leeuwen et al., 1999: p.489). Bibliometrics is usually used to study structures, such as citation and co-citation maps within and between disciplines, and more generally for visualizing literatures (Borgman, 2000: p.147). Bibliometric indicators may be also used as a measure of comparison within a specific discipline (King, 2004).

Here bibliometrics was used to help in the acquisition of the material used for the analysis of the scientific discourse as well as for mapping collaboration patterns between scientists (through their country of affiliation) within forest science.

3.1.3.1.PUBLICATIONS AS RESEARCH OBJECT

Scientific communication may be carried out both in formal and informal ways. Contact with colleges, entries in blogs, and personal communications count mainly to the latter form of scientific communication, while books, articles in scientific journals, and contributions in conferences count to the former. To focus research on one product of the scientific communication is to narrow down the generalizations that research conclusions may arrive at. However, to carry out research on all products of scientific

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communication is an enormous task that exceeds the possibilities of this work. Thus, the question becomes which product of the scientific communication process discourse analysis should be carried out upon.

Scientific articles in science journals are seen as the central product of natural sciences.

True knowledge will only be seen as such if research results circulate within the community, in other words: what is not received will not be seen (Weingart, 2001: p.100).

Additionally, publications in the form of articles in journals are an acceptable research object because they represent parts or complete processes of problem-solving research (Qin et al., 1997: p.894): they are post hoc in nature and emphasize the end-product of research. The central assumption widely accepted is that when scientists have something important to say they do so by vigorously publishing their findings in the open international journal literature22 (van Raan, 2004: p.26). The assumption that research results can be considered true scientific knowledge only after they have been made public (accessible) and made available for scrutiny by the entire scientific community -and in such a way contributed to the growth of the total stock of knowledge- is largely accepted where scientific publications are seen as recognition of the accomplishment in science (Weingart and Winterhager, 1984: p.98). Forest science is part and is largely dominated by natural science. This has consequences for the communication process of forest science, as research emanated from this field is as well largely disseminated by scientific publications (as results from a survey carried out in the IUFRO World Congress 2005 in Brisbane, Australia showed). There for the assumption that forest science research results can be considered scientific knowledge only after it has been published, holds true. And so, to carry out discourse analysis having as object of research scientific articles will deliver results that represent the scientific discourse on forest.

However, not all scientific articles published in all scientific journals can be examined. A prioritization of the publications to be analyzed must be made. Citations indicators are used as a means of ranking and selecting the articles to be analyzed. Citations are an indicator that scientific publications are read and the information within it processed, in other words that articles are identifiable objects of the scientific communication (Weingart, 2001: p.104). Publications that are not cited are lost in the communication process. Analyses have shown that more than half of the articles published will never be cited (Garfield in Weingart, 2001: p.105). Observation has also shown that there is an 80/20 rule in citation analysis: about 80% of all citations go to only 20% of all articles published.

For this analysis for each year within the time frame analyzed (1994-2003) a ranking based on citations was made (see material section for more details).

3.1.3.2.LIMITATIONS OF CITATIONS

King (2004), when analyzing the quantity and quality of science for different nations, notes that a potential problem for using citation data is that individual papers may skew the results of an analysis. This happens when an article has been highly cited because it

22 It is widely accepted but as well biased to those disciplines where publications are the main carrier of scientific knowledge (van Raan, 2004: p.26).

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has been discredited or because its authors over-cite their own work (King, 2004: p.311).

The first problem has not been dealt with in this work because the objective was not to examine papers which have had a good or bad impact on forest science, but to examine papers that have influenced forest science in any way. If a paper has been discredited, it has at least served the discussion of the issue and it may be the source of creation of

„better‟ knowledge. The second problem regarding authors over-citing their own work was dealt with by eliminating the self-citations²³ the author or authors of the given paper.

Other bias that has to be considered is the language bias. The citations delivered by the Scientific Citation Index²⁴ (SCI), even though very selective, are biased to English language journals (Arvanitis and Chatelin, 1988: p.114). Journals and articles which are written in other languages (e.g. Spanish) are not incorporated into the database, necessarily influencing the number of citations an article may receive. This is especially important when considering articles written by authors from developing countries. Their research findings published by peer-reviewed journals may be cited relatively more frequently in national journals that are not part of the journals considered in the SCI database.

Consequently, bibliometric awareness of an article does not necessarily equate to peer-awareness (van Raan, 2004: p.36).

Arvanitis and Chatelin (1988) have carried out analysis for tropical soil science. They concentrated on examining the national strategies of developing countries on publishing their research results finding, that the scientific production of Third World countries is actually higher than usually estimated through tools such as the SCI. Their analysis of scientific production, based on the multidisciplinary database PASCAL, produced results that contradict studies using ISI-database (articles published in scientific journals forming part of the data base of the Web of Science) regarding the inclusion of Third World countries in scientific discussions (Arvanitis and Chatelin, 1988: p.115). For the year 1983, they found that the South produced half the research on tropical areas (or 11% of the total of the world research in agricultural science). This number goes beyond the 6%

usually admitted for Third World countries (Garfield, 1983) based on SCI. Therefore this is an indicator that science produced in countries outside the scope of journal-countries appearing in the database of the Web of Science is underrepresented. Using a database like the one used by that study would of course reduce the bias of mainstream science (or English written literature) however, and because PASCAL is a database created for pure scientific reasons and not bibliometric ones, no indicators of the publication‟s impact are available through it. Thus, no statement could be made regarding the international impact of scientific articles and their contribution to the global discussion of the topics and thus to discourse.

The „Sleeping Beauty in Science‟ phenomenon is another aspect that must be considered when using citations as indicator of importance. A publication may go unnoticed (may be sleeping) for a long time and then, almost suddenly, it attracts a great amount of attention (it is awakened). Thus, articles may be ignored in the debates of topics because they have yet to be awakened. However, for this analysis this bias does not alter the conclusions arrived at, because the analysis here undergone were made considering the citations of

23 Self-citations are citations given by the authors of publications to their work in consequent work. This may negatively influence the citation index of an article as there is a risk that authors cite their own work in order to appear centrally involved in the scientific discussions.

24 Owned by Thomson Scientific.

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articles until a specific date in time, namely December 2005. Thus, the results are based on the dominant discussion (or discourse) carried out till that specific time-frame. Articles that were dormant and have since then woken up will not have influenced the scientific discussion at the time.

3.1.3.3.COLLABORATION

Collaboration in forest science is here measured as multi-authored articles that have been published in forestry journals (both international and national). As an indicator for collaboration, multi-authorship is contested as an adequate measure. However, this indicator has generally been considered as an unobtrusive indicator of collaboration (Gordon, 1980) and has frequently been the source of scientific studies on collaboration (Katz and Martin, 1995; Bordons and Gómez, 2000; Arunachalam, 2000; Wagner and Leydesdorff, 2005; and Wagner, 2008; amongst many others). In these previous studies, collaboration was examined through articles extracted from the Science Citation Index;

networks were built and analyzed according to the affiliation organizations of the individual scientists and countries where these organizations are located.

Collaboration was characterized according to the affiliation institutions and affiliation country the authors of the scientific articles belonged to. According to this, classification of an article would fall into one of the following 5 categories (Qin et al., 1997: p.897): 1) no collaboration; 2) collaboration in a department; 3) collaboration between two or more departments within an institution; 4) collaboration between two or more institutions within a country; and 5) international collaboration.

If international collaboration took place, then the affiliation countries of the scientists were used to map the collaboration between countries. When an article was a result of collaborative work between two or more countries, then a record was added to each of the countries that appeared. This method for measurement is known as the whole-count (or normal count) method (Lindsey, 1980; Wagner and Leydesdorff, 2005; Wagner, 2008) and is widely used in bibliometric analysis. If an author had more than one affiliation institution and/or affiliated to more than one country, the first institution/country to be mentioned was the one included, others were disregarded.

In order to map Center-Periphery structures and collaboration patterns, the data gathered for the specific publications was considered. Sociometry or network analysis gives information on the structure of a group, the position of individual group members, and the informal structure of the group (Friedrichs, 1973: p.255). Here the first two elements are important because the aim is to examine the structure of the group of scientists participating in forest science and the position of individual countries (affiliation countries of the scientists) within the structure of collaboration.

Social network analysis was used as a means to identify the relations of collaboration in the networks of forest science, as well as the productivity and geographical distribution within the scientific discourse. The productivity of the networks in forest science sheds lights on the existence of Centers and Peripheries in the world forest science. The collaboration networks gives insight on the countries which are more or less active in

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their collaborative efforts (who collaborates?) and the collaboration ties that are present (who collaborates with whom?).

Graphic visualizations of the collaboration taking place in forest science were carried out.

Such network visualizations have been carried out by many authors in their efforts to map world science, specific disciplines, and individual scientists‟ collaboration patterns;

amongst many other analyses (Katz, 1994; Hara et al., 2003; Wagner and Leydesdorff, 2005; Wagner, 2008).

Regarding the nodes (elements of the networks) to be considered for revealing both the Center-Periphery structures as the collaboration patterns, the affiliation country of the authors of the scientific publications were used: this was the same data as the one used to reveal Center-Periphery structures. Regarding collaboration, links were established between the nodes when articles were co-authored by scientists from different countries.