date: 2014-02-07 16:18
Some notes Polanyi and the Epistemology of Science.
There are a number of ways to read Polanyi (2009), and the knowledge management literature has taken off with the very singular aspect of his work – that “we can know more than what we can tell” (p. 4) (as well as Nonaka's 1) version of tacit knowledge), and much of the focus has been on business and organizational management. However, Polanyi's idea has an important implication for the nature and work of science, with implications for science policy, and Polanyi discusses this in some detail in the third chapter.
We can relate the implication to some modern developments. There is a growing number of researchers who are actively pursuing a thing called open science 2). Some of the arguments put forth in favor of a process that opens science include claims that an open science will result in a more efficient system that takes advantage of web and Internet technologies, is able to operate with better transparency, is better at re-using other scientists' data, and is better at attributing other scientists' work (e.g., see Woelfle, Olliaro, and Todd, 2011 3)). In some ways, we might say that an open science is the full, or nearly full, realization of the inherent norms of scientific practice.
In this rhetoric among the proponents of open science lies the idea that an open science results in a science that is more scientific. The key idea here is that science practiced openly will be more available for critique, review, judgment, and so forth and as such, its epistemological claims can more easily be falsified 4) or verified 5) via, usually, reproducibility 6) (or more appropriately, a test of commensurability rather than a test of verification). Thus, and at the very least, there are ideas inherent in the open science movement that imply that science is not science unless it is open and it is not true unless it is reproducible, in a very mechanical sense.
As a result of these claims, the suggested move forward in open science is to test whether an open science results in a better quality science (e.g., does data sharing lead to better scientific outcomes, etc.). But this largely ignores some of the epistemological problems with commensurability 7). Furthermore, and pertinent to this, is one basic issue: the criterion of demarcation 8). That is, what criterion can we use to identify science from what is not science (or pseudoscience)? This is an important epistemological issue for Polanyi — if “we can know more than we can tell” means we can communicate only so much of what we know, and open science is about being better at communicating scientific work, then even under a better model of scholarly and scientific communication there will always be an upper bound limit on what can be falsified or commensurated. This means there will never be a complete guarantee that scientific claims can be trusted.
What is also not fully acknowledged are other ideas about the demarcation of science–issues related to problem-solving (ala Kuhn 9)) and problem-finding (ala Merton). Here we can refer to, I think, one of the best passages (pp. 64-66) of Polanyi's book, the ending of which he writes:
Thus the scientific interest–or scientific value–of a contribution is formed by three factors: its exactitude, its systematic importance, and the intrinsic interest of its subject matter (p. 66).
In other words, Polanyi argues that we can demarcate science not just by its truthfulness, its coherence, but also by whether it's interesting in the right theoretical way.
In later posts, I'll write more about the commensurability issue–as it relates to Polanyi's description of tacit knowledge.
Revision date: 2015-01-21 10)
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14-37. doi: http://dx.doi.org/10.1287/orsc.5.1.14
Polanyi, Michael. (2009). The tacit dimension. Chicago: University of Chicago Press. (Original work published 1966) URL: http://www.worldcat.org/isbn/0226672980
Woelfle, M., Olliaro, P., & Todd, Matthew H. (2011). Open science is a research accelerator. Nature Chemistry, 3(10), 745-748. doi: http://dx.doi.org/10.1038/nchem.1149