Abstract
The question of the division of cognitive labor (DCL) has given rise to various models characterizing the way scientists should distribute their efforts. These models often consider the scientific community as a self-governed sphere constituted by rational agents making choices on the basis of fixed rules. Such models have recently been criticized for not taking into account the real mechanisms of science funding. Hence, the question of the utility of the DCL models in guiding science policy remains an open one. In this paper, we show that two unconsidered dimensions would have to be taken into account. First, DCL studies miss the existence of distinct levels of epistemic objectives organizing the research process. Indeed, the scientific field is structured as a system of hierarchical, interconnected practices which are defined both by their inherent purposes and by various superposed external functions. Second, I criticize the absence of ontological considerations, since the epistemological significance of pluralism is highly dependent on the nature of the object under study. Because of these missing dimensions, current DCL models might have a limited usefulness to identify good practices of research governance.
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Notes
See, for instance, Wilholt and Glimell (2011) for a synthetic historical panorama.
Of course, the question of the governance of science is not limited to that of the processes of grant allocation by funding agencies. Our paper exclusively deals with epistemic considerations, but ethics and political philosophy also have their say. The so-called “responsibility” of research and innovation (Arip 2016), and the democratic involvement of citizens in science policy decisions (Kitcher 2001) constitute central perspectives on this matter.
Devoted to the “assessment of the epistemic consequences of adopting certain institutional arrangements or systemic relations as opposed to alternatives”.
Some instances of this interpretative prudency, among others: “Can our models fix useful concepts and provide templates for causal mechanisms that could be at play? Could they be used to help shape the debate around emerging policy decisions? The answers will come from future work in the field” (Avin 2018b, p.32); “The cautious conclusion to be drawn from these differences is that, in its entirety, the relationship between diversity and epistemic performance is likely to be more complex than can be captured by any simple model” (Pöyhönen 2016, p. 4530); “Of course these models are limited in two critical ways. First, they proceed into idealizations about the structure of the scientific community and about individual scientists. Real scientists and scientific communities are more complicated than our models, and it is always possible that a critical causal factor has been left out” (Zollman 2018, p. 26).
Let us maintain, following Kelly (2003), that instrumental rationality classically designates “the rationality which one displays in taking the means to one’s ends” (p. 612)”. It is opposed to epistemic rationality, defined as “the kind of rationality which ones displays when one believes propositions that are strongly supported by one’s evidence and refrains from believing propositions that are improbable given one’s evidence”. In DCL models, as noted by Zollman (2018), epistemic rationality is understood as a form of instrumental rationality: “epistemic rationality is a species of instrumental rationality, viz. instrumental rationality in the service of one’s cognitive or epistemic goals” (Kelly (2003), p. 613). In other words, epistemic rationality is expected to be mobilized to choose the best alternative (the Mi which has the estimated highest probability to reach O) to solve a cognitive or epistemic problem. This instrumentalist conception of epistemic rationality corresponds to the multiplicity of the (cognitive, practical, utilitarian) kinds of objectives considered in the DCL models.
The opposition between “epistemic” and “non-epistemic” factors may be discussed, since the objective of DCL models is precisely to show that “non –epistemic” factors positively influence the search for truth. However, the rationale behind this distinction lies on a contrast between the motivations guiding the choices made by researchers, as well exposed in Zollman (2018). If these motivations are exclusively those of solving the problems posed to the community, they are considered as acting as purely epistemic agents –even if this problem is not itself strictly cognitive.
For a graphical representation of this hierarchical imbrication of activities, see the “commentary” on Chang’s work by L. Soler and R. Catinaud in Soler et al. (2014).
This exhaustive examination may be completed, in the spirit of Kitcher (2001), by a democratic assessment of the various desires expressed by ordinary citizens.
We may also imagine here that publics which are exterior to the scientific field (citizens, economic sphere or political actors) could also have their say in this process of proposing and voting for projects.
If we follow the examples provided by the authors, these objectives may be to cure peptic ulcer, to make astronomical predictions of the “locations of heavenly bodies” in 1550 (by choosing between Ptolemaic or Copernican paradigms, p. 1063) etc.
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This article belongs to the Topical Collection: EPSA17: Selected papers from the biannual conference in Exeter
Guest Editors: Thomas Reydon, David Teira, Adam Toon
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Bedessem, B. The division of cognitive labor: two missing dimensions of the debate. Euro Jnl Phil Sci 9, 3 (2019). https://doi.org/10.1007/s13194-018-0230-8
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DOI: https://doi.org/10.1007/s13194-018-0230-8