Drinking the Kool-Aid while attempting to be an unbiased researcher

I realized today that I’ve spend the past week and a half drinking the Bioversity Kool-Aid; meaning that I’ve been surrounded by great ideas like “custodian farmers”; researchers who are really interested in “farmer first” technologies, networks, and access to resources; and in the past few days, getting a chance to talk to farmers who are involved in Bioversity’s projects here in India. Which is all great, especially considering that Bioversity’s mission aligns with my own ideological commitments. But this is something my academic committee has pushed me to think about: how will I deal with my own pro-farmer, pro-local biases when conducting my research?

One of the things I’m studying is the difference between/evolution of the scientific paradigms of “wide adaptation of crops/top-down technology transfer” and “location-specific adaptation/participatory research.” The first approach is dominant in the state-run agricultural organizations; the second (I’m hypothesizing) is more likely to crop up among NGOs and individual researchers. But, is one approach necessarily “better”? My own bias pushes me towards thinking the second approach is better, because it takes into account the local socio-economic as well as environmental conditions of farmers. But from an innovation systems perspective, we probably need some combination of both approaches. The first approach works well for large farmers on productive lands; the second works better for marginal farmers/areas. I think if I’m able to take a more even-handed (or “academically agnostic”) approach to my research, my scholarship will be better. One step I can take to reduce my bias is to be very careful in my interviews, and to carefully consider what my respondents say. In my historical research, I think the best strategy for now is just to document all relevant material, and then sort out my theoretical argument later.

Theoretically, I find the correlation of the “wide adaptation/top-down” approach with a socialized political system extremely interesting. This would require me digging into more political science than I currently have under my belt, but I think it would be really interesting to compare 20^th century agricultural development in the US, Soviet Russia, and India. For example, what was the political context; proportion of public vs. private research investment; scientific paradigms; and ultimately the success of agricultural technologies in each of these nation-states during critical periods of agricultural development? Maybe I can find someone already working on this stuff and collaborate…

In other news, baby goats are the cutest things ever and I want one!

3) Public participation in science: co-production of knowledge

Image source.

An important theme of Science & Technology Studies is the "co-production of knowledge." When I first started my graduate studies at ASU, this word threw me for a loop because it packs so much meaning into one phrase, but I guess that's the purpose of academic jargon! The co-production of knowledge means, according to Sheila Jasanoff, "the proposition that the ways in which we know and represent the world (both nature and society) are inseparable from the ways we choose to live in it" (Jasanoff, 2004, p. 2).

Jasanoff's definition incorporates how knowledge shapes both science and social order (sometimes simply described as the co-production of science and policy or society). How we represent knowledge-- through charts and graphs, DNA samples, lie detector tests, brain scans, environmental impact assessments, maps, etc.-- has implications for not just science, but society. For example, a map of climate change vulnerability can show the scientific results of a study, but it also holds implicit values and political implications.

The creators of this climate change vulnerability map chose not to include most developed countries in their assessment, which certainly has impacts of how we view climate change. For example, climate change is sometimes viewed as a problem mostly facing developing countries, and this map reinforces this. Co-production of knowledge and social order goes beyond just media representations of science; it is deeply important for our political system, and how we make decisions based on science.

We often think of science as a top-down, self-regulating hierarchy of experts. But when science gets used in decision-making, it must conform to the ideals of democracy. Science cannot dictate policy decisions, but can be a useful political tool. Thus, incorporating the "co-production" perspective of science and society can make the role of science more clear in these situations, rather than the muddled role it currently takes. For example, Roger Pielke Jr. shows how the Intergovernmental Panel on Climate Change (IPCC) ignored the "co-production of knowledge" and instead engaged in what he calls "stealth advocacy" of policy. If the IPCC was more upfront about the political implications of their climate change assessments, they would act as more of an "honest broker" of the co-production of science and policy.

Several scholars have examined climate change knowledge from a co-production perspective. Vogel et al. (2007) examine the connections between co-production and science communication. Commenting on many of the topics previously discussed in this blog, they use the case study of food security and climate vulnerability assessments in southern Africa, and how crossing the science-practice boundary through stakeholder engagement resulted in more useful assessments that could be utilized by local organizations and governments. By recognizing the needs of stakeholders, the knowledge gained from these assessments can be used in more democratic ways.

Lemos and Morehouse (2005) look at the case study of NOAA's regional integrated science assessment (RISA) program (coincidentally, the Great Lakes basin now has a GLISA program). The Southwest RISA used a process of stakeholder dialogues to produce a regional assessment of climate change impacts that was relevant and useful to end users. The authors write,

"Co-production of science and policy in the context of integrated assessment activities requires substantial commitment to the three components we have identified: interdisciplinarity, stakeholder participation, and production of knowledge that is demonstrably usable." (Lemos & Morehouse, 2005, p. 66)

In this case, the RISA aimed to produce scientific knowledge about climate change that would be useful for decision-makers like farmers and policy-makers. Without the stakeholder participation, the researchers would not have known how the information would be applied, and how to shape their research based on this. For a more detailed description of other RISA climate change programs, see this report.

For the climate change and agriculture project I'm working on with Michigan State University Extension and Kellogg Biological Station, we are trying to follow the model of stakeholder participation from the start. This is based partly on the work of our colleagues in the Southeast climate RISA and participatory process used to create their AgroClimate website. There are also great examples of using participatory focus groups and community dialogues in forestry and bioethics. Personally, I can tell you that something I've already learned is how important social science research is to this process. The natural sciences can tell us a lot about our world, but social science helps tell us what decision-makers need for knowledge and support. The results are often surprising and definitely eye-opening for those of use entrenched in academia. And this is why co-production of knowledge is important! Recognizing that the process of knowledge production (aka science) is just as important as the end results, and that how the end results are used is often pervasively social and political, STS can lend us some valuable insights for practical results.