Data mining Science policy
Data mining: The value of a scoping phase
August 16, 2017
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In previous posts in our data mining series, we laid out our initial technical framework for guiding data mining projects, then supplemented that with plug-ins to facilitate its use for R&I policy research specifically. These plug-ins helped to overcome the challenge of applying a generic framework to a specific thematic area. However, there was another major challenge that we identified in using data mining for policy research, and this difficulty prompted another revision: the reorientation of the framework to include a scoping phase. This week’s post explores that challenge and how the reorientation helps to solve it. Take a look!

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Data mining Science policy
Data mining: Technical framework plug-ins for the R&I context
August 9, 2017
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In my previous post, I outlined the initial technical framework developed by Science-Metrix in the course of the data mining project for the European Commission documented in this blog series. This initial data mining framework—strongly inspired by existing frameworks—provided a solid foundation on which to build. However, to support data mining in a policy context in particular, and research & innovation policy specifically, further development was needed. This post covers some of the more novel work we undertook in this project, bringing new suggestions into the data mining space. There were two main drivers for these further developments; the generality of the framework is treated here, and its feedback loops will be covered next week.

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Data mining Science policy
Data mining: The root of a technical framework
August 2, 2017
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Continuing on in our series of posts on data mining for policymaking, this post presents the initial technical framework developed by Science-Metrix to guide the conduct of data mining projects in a government context (with some shout-outs to other contexts as well). This seven-step framework formed the basis of our case studies, and effectively lays out the steps through which data mining projects progress from stem to stern. It’s a great introduction for those who have never participated in data mining. For more experienced practitioners, the framework provides the basis for understanding the main challenges to data mining in a policy context, as well as the recommendations we put forward to address those challenges and get more value out of the process.

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Data mining Science policy
Data mining for policymaking
July 26, 2017
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Throughout 2015 and 2016, we at Science-Metrix worked on a project for the European Commission that focused on data mining and big data analytics in the context of policymaking, specifically research & innovation policy. While carrying out this work, we learned some fascinating and valuable things, and so rather than leave all that knowledge locked up in a full report that’s hundreds of pages long (before annexes!), we’re sharing the key insights from the project through a series of blog posts. Here’s a free sample to pique your interest: it’s highly valuable to cross big data sources with more established sources that are better understood. This introductory post outlines the context of the project; stay tuned for the rest of the series! (more…)

Higher education Science policy
Policy: whose problem is it anyway?
March 14, 2017
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In January, Sir Peter Gluckman—Chief Science Advisor to the PM of New Zealand, and global point man for science advice to government—gave the inaugural address at the Canadian Science Policy Centre lecture series. The discussion covered a lot of important points of difficulty for science and governance—and science in governance—that are emerging in the 21st century as a result of the rapid development of information communication technologies. In this post, I’ll recap a few of his main points (with my usual editorial gusto), and add further detail to one point that I felt was ambiguous and on which it’s worth getting clear. (more…)

Higher education Science policy
Committee Outsiders: a quick win
March 7, 2017
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In a previous post on the need to “operationalize” policy questions into a format suitable for empirical research, I ended with a call to action for the community of academic historians and philosophers of science to come down from our ivory towers, roll up our sleeves, and apply our skills to mediate negotiations taking place at the science–policy interface. But what exactly does that call to action entail? If you’re in that crowd, and are convinced by the arguments I put forward, what is it that I’m urging you to do, exactly? (more…)

Science policy
Capturing imaginations, not wallets and podiums
February 28, 2017
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The notion of capture—when one group in a partnership is allowed “home-field advantage”—is helpful in understanding some hurdles to successful collaboration across disciplinary and sectoral boundaries. Last week, I outlined how sectoral capture undermines the very notion of transdisciplinary research. In this week’s installment of the capture series, I’ll talk about how sectoral capture is rampant in the way that the research sphere engages with the political sphere, on two big-ticket issues specifically. (more…)

Higher education Science policy
Transdisciplinary research: a recipe for sectoral capture
February 21, 2017
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Now that I’ve put pen to paper and presented the notion of sectoral capture, I can finally put it to use! In this post, I’ll be exploring how sectoral capture is not only a huge risk in transdisciplinary research, but is actually embedded in the very definition of transdisciplinary research itself, calling for us to rethink that activity and find it a more appropriate name. Of course, for this we’ll need to explore the definition of transdisciplinary research, which is where this post starts off. (more…)

Higher education Science policy
Flagging problems of capture
February 14, 2017
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The notion of disciplinary capture is a very useful conceptual tool, introduced in slightly different contexts: Brister (2016) in relation to the dynamics within interdisciplinary teams, and Frodeman, Briggle & Holbrook (2012) as well as Briggle and Frodeman (2016) in relation to the way that departmental and other academic structures influence the direction of philosophical inquiry. In this post, I’ll unpack the notion of disciplinary capture, and introduce a new analogue: “sectoral capture.” The notion of sectoral capture will help us to grapple with a whole range of problems, to be explored in future posts. (more…)

Bibliometrics Database Higher education Leiden Manifesto Science policy
Interdisciplinarity: the mutual adjustment of concept and indicator
December 8, 2016
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In their recently released report, Digital Science describe and assess the relationship between a range of candidate indicators for interdisciplinarity. Their objective is to assess the consistency between the indicators and attempt to discern a front runner in this race. Ultimately, though, they conclude that the indicators produce inconsistent findings, and finish with some remarks about the responsible use of research metrics, many of which are important and timely. Along the way, though, I think they make an important—but tacit—assumption about interdisciplinarity, one that’s worth bringing to the surface to flesh out the picture of how to responsibly overcome the difficulty in measuring and adopting interdisciplinary approaches to research. (more…)