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Collaboration Types and Patterns of Trust

When one thinks about RN markers for collaboration, literature is quite sparse. Regarding the issue, Paul Thagard in 1997 brought a standard for identifying the types of collaboration. In our view, he designed patterns that could be used in the evaluation of research collaboration networks. In his studies, he drew attention to the social aspects of knowledge production in coauthorships. He analyzed the top journal publications in different fields of scientific knowledge and noted that coauthorships and collaboration were common and intense in the natural sciences. However, they were rare or reduced in the areas of humanities. From observation, he studied the nature of collaboration and devised four ideal types: employer/employee collaboration; teacher/learner; and similar peer/peer-different. He clarified that ideal types would not have defined boundaries.

If scientists have good reasons to work together, Thagard said, there are also good reasons to understand and evaluate their social practices, and for this, there should be a common denominator. Thagard understands that collaboration is based on the search for truth. In veristic terms, there would be a list of five patterns, namely trust, power, fecundity, speed and efficiency, which characterize true patterns of collaboration in science. Its opposite, in science, would be the error, and in the view of scientific realism, a false result.

The scientist searches for answers to questions and hypothesis, theoretical, practical, or observational research results, or all of these and many others. The results must be true if, in the short term, they are accepted by fellow scientists, if publishable in good and reputed journals with referees, and, in the long term, if they contribute to the progress of science and the welfare of humanity. However, only veristic standards are not sufficient to measure collaboration. Thagard then suggested merging these categories with the ideal types of collaboration and examined losses and gains experienced by scientists working in collaboration. Table 3.1 presents an overview of the ideal types of collaboration with truthfulness patterns, based on Thagard (1997).

The intensities of these different kinds of collaboration generate different results. In practice, it is necessary to balance the advantages and disadvantages of each level taking into account the results to be obtained in a certain period of time.

We are living in a collaborative research age (Adams, 2013), a period in the history of science when collaborative networks overtake local spaces, national territories, and spread to the most recondite global spaces followed by invisible colleges. It seems to be easier and faster to develop new ideas through dialogue than through individual work. Collaboration departing from a single RN echoes to numerous voices all over the world. In doing so, it disseminates science and ideas, being a realistic driver of excellence for higher education.

In this chapter, selected authors representing an extensive literature brought us a theoretical approach in order to show that there are still unknown spaces on the forms of collaboration. As explained, there have been much discussion and studies on networks, but for innovation purposes and not for evaluation purposes. What to say about the evaluation of the funded research with public money or the formal RNs that were created with public resources if they are not monitored? Considering the CRN as human spaces influenced by social, psychological, and scientific

Table 3.1 Types of collaboration

Types of collaboration




This would be the weakest form of collaboration. In a laboratory that employs technicians, assistants, interns, and others, the category reliability may or may not be expected. The group labor division will yield time gains for the research. However, even with good “employees” the compensations among losses in reliability and gains in power, speed, and efficiency must be considered. Fecundity, the ability to get more results the larger the number of people, seems not to be relevant in the evaluation of this kind of collaboration.



This would be the most common kind ofcollaboration in sciences. In humanities, in general, students may work in projects that are not linked to their advisers. However, the same does not occur in the hard sciences, in which the student is crucial to the development of the adviser’s project. Experienced scientists work with their advisees not only to increase productivity, but to train new scientists and train them in the tasks ofthe scientific field. Doing science requires the know-how to design experiments, build apparatus, use software, interpret statistics, and employ mathematical formulae. This kind of collaboration involves fecundity. Scientists have grants they may use to hire their students and assistants. In humanities, there is no labor division because research cannot be divided into parts; students may receive funding without working with their advisers. Tradition also plays its role. Ifnewcomer professors have not worked with their advisers, they will not work collaboratively with their students either. On the contrary, in Physics, for instance, reproduction is guaranteed: advisees will work with their advisers for a long time. Research costs are so high that new doctors will take many years to research with independence from their advisers. Young researchers, in the meantime, will have difficulty to obtain the resources they need to acquire status in their careers. At the same time, they will have difficulty in being equally credited for the same work in a coauthored publication.


Researchers with the same interests and training may benefit from collaborating with each other. Even with similar backgrounds, two heads working together think better than two heads working alone. Moreover, members of a networks community can be reassured in a decision or a result. However, similarity may be harmful when reliability lies only in the confidence ofthe main researcher. For instance, it is even possible to propagate frauds and mistakes because ofthe epistemological dependence among researchers and ofthe trust devoted to known people and groups. In this case, one researcher’s misstep may compromise the validity of the group’s entire work. As for the benefits of peer-similar collaboration, researchers may benefit from the exchange oftheoretical exploits and new experimental designs. It is easier and faster to develop new ideas through dialogue than through individual, lone work.

Table 3.1 (continued)

Types of collaboration



Colleagues from different knowledge areas or interdisciplinary fields have a potential gain in fecundity. They may achieve more robust results. There are gains of power and speed from the combination of theories and/or methodologies. Peer-different collaboration contributes to stimulate reliability. However, scientists must understand each other since the differences among the fields must be recognized and respected by all. Trust must be built among colleagues of different fields as there is no way to critically validate results from unfamiliar methodologies.

Source: Based on Thagard (1997)

structures, it is useful and relevant to analyze these settings with an educational and pedagogical view to better understand and evaluate them. The evaluation of RNs becomes crucial when we realize that the production of knowledge has a growing tendency to rely on science- oriented systems for relevant strategic social objectives.

Hence, collaboration has always been an asset in research work, but there is a change happening. From a traditional, hierarchical standpoint on the academic research, evolution takes on the shape of flatter labor structures. Networks, formal and virtual, allow knowledge and information flow to be even more fluid and fast. Within RNs, collaboration networks and knowledge networks are built and developed in mutual reinforcement.

The next chapter addresses researchers’ speeches about networks, coming from different disciplinary fields. It also discusses what researchers say about their work and what the analysis of network graphs showed to them. We discuss their speeches taking into account their different approaches of collaboration and networks. Later, in the following chapters, we will discuss how we can evaluate RNs and value researchers’ works. We will show the relative importance of the rankings and what we need to know about evaluation indicators and measures that serve to improve the coauthorship networks in order to better know them and, if necessary, to make informed choices.


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