Whose Knowledge Counts Part 1

This slide deck is part one of Whose Knowledge Counts. By the end of the slide deck, students should be able to identify why asking whose knowledge counts is crucial when we build and use data. Illustrate how the concept of sustainability integrates multiple expert knowledges which can still compete for attention within research agendas. Describe how new research methods and tools shift which perspectives become data and map the human and non-human actors that shape what we count as valid knowledge.

We have major food and agricultural problems today. First of all, not everyone has access to healthy foods in our current system. And some of our agricultural practices harm food, people, and planet. And that's why today's challenges are not just about growing more food for people, but it's also about growing better for people and planet.

Um, I'm going to give you an example of one of the uh problems that we have with agriculture that can affect everything. Um, there's soil and water trouble, right? So you have this um example of erosion, nutrient runoff, and unpredictable rains that occur, and sometimes they occur altogether. When fields are tilled or left bare between crops, wind and rain wash away the topsoil that holds nutrients and water. This loss of topsoil means less fertile land, forcing farmers to apply more fertilizer or altogether abandon these worn-out fields. To replace this lost fertility, many farms rely on synthetic fertilizers. But heavy rains can wash excess nitrogen and phosphorus from these synthetic fertilizers into the streams and rivers. That runoff fuels algal blooms downstream, poisoning fish, creating dead zones, and forcing costly water treatment upgrades. And then you have the unpredictable rains. Climate change intensifies both droughts and downpours without reliable rainfall patterns. Planting schedules get thrown off, seeds may drown um or never sprout, and irrigation demands spike. So farmers can't plant effectively, feel their yields um, or they have lower yields, their yields drop, and both soil structure and nearby waterways suffer when heavy floods scour the land.

These challenges show why um these conventional "grow more with more inputs" advice often backfires. And let me uh, now we're going to trace back where this sort of thing comes from. This kind of this sort of knowledge comes from. So you have these extension services, they're called. So these standard extension services are often missing the mark with trying to grow better for people on planet because they haven't factored in these local realities. Extension services are the outreach arm of land-grant land-grant universities. Is there a classification of universities called land-grant universities where um agricultural uh knowledge and military and um some other practical uh technology-based knowledge for the public good um are the core of the university? Okay. So these extension services are the outreach arm of land-grant universities and USDA, the US, the United States Department of Agriculture, where they send out agents into the field to show farmers new techniques. But when those agents promote a single package of practices for an entire region, a number of things can happen, like small-scale or specialty growers can get left out. Um, these conventional research and development approaches often chase higher output, higher yields at the expense of ecosystems and communities. Smallholders face these one-size-fits-all recommendations that don't fit local soils, markets, or traditions.

So together, these challenges show why conventional "grow more with more inputs" advice often backfires, making soils less resilient, polluting water, leaving farmers at the mercy of an unstable climate. This is just one example. There are a number of other things that that's that's problematic with uh with our agriculture, like labor issues um with um the the use of chemicals like we mentioned um and equality, right? Who who has access to healthy foods? Um, there's biodiversity loss. Uh, like I said, this is an example of environmental impact, but then there's also social labor labor concerns like farmworkers often enduring low wages, poor working conditions. These are all um these all affect one another. It's a very uh it's probably something that uh you have already learned or you could learn in the food systems class that is also offered in our STS department. Um, it's a very exciting it's a very exciting topic and and um there's it's a very serious topic. Um, but in any case, this is just an example. So I've just given you an example of what happens, what the environmental impact can be with with um uh with our current agricultural practices, right? Um, and how farmers um it can be very local, local specific, localized, very uh location-specific what's happening. And so when you have um extension services that uh are are meant to improve the system, it's the the question should be, whose knowledge, whose knowledge is being disseminated here? What is this extension um these extension services where there where does their knowledge come from? What are the goals of their knowledge? Is it to uh make sure that we're always thinking about the greatest output? Um, are there any tradeoffs that are considered? Um, okay.

And so this is what we're going to talk about in the next few slides. Okay. So try to keep these threads together. I've talked about a problem, a public problem. Um, I've talked about uh a solution that comes from a specific uh knowledge path. Um, and it doesn't necessarily fix the problem, right? And but there are ways to look at this, and we're going to talk about this one particular study that that thinks through through this very well. Okay.

So the United States Department of Agriculture, the USDA, has um a program called Sustainable Agriculture Research and Education, and um it's been around since 1988. It flips this extension model. What it does is it says instead of experts telling farmers, Sarah says farmer and scientists will co-design the experiment together. Okay, they're going to um be collaborators. So rather than sending um top-down fact sheets, SAR grants require um so it's a it's a program that gives grants. Okay. So, they fund place-based projects that balance project uh profit. They they balance profit, ecology, and community well-being. Why is this why are they balancing these three things? And so, I will talk about that when we define sustainability. But this is the goal. They fund place-based projects that balance profit, ecology, and community well-being. Okay. So rather than sending their top uh what I was saying before, rather than sending top-down fact sheets, SAR grants require farmers, researchers, and local educators. Okay, so you have farmers, you have researchers, and you have local educators to sit down together and decide, what's your biggest problem? How do we measure success? That collaboration creates data and practices that actually fit real farms. Okay.

So keep this in the context of what we're talking about, whose knowledge counts. SAR is saying that in the past, it's been experts, like maybe uh scientists, right? Uh, researchers, uh, people in the chemistry labs that are being funded by some major insecticide pesticide company. Who knows? But um, SAR is saying that farmer knowledge also counts here. So they're bringing in farmers, researchers, and local educators to sit down together and decide, what's your biggest problem, right? So they're defining together what is the biggest problem. They're defining it together, and then they're saying, how do we measure the success, right? How do we me how do we um uh figure out if what we implement is uh works. Okay.

So sustainability, we're going to talk about sustainability. There are the three legs of sustainability. We all know about economic, right? The economic leg. This is profitable farms that keep growers in business. Okay. So um, when why do we talk about sustainability? The we we we talk about sustainability to say uh it's not important to have these uh solutions that we can put in place and fix a problem and maybe create a bigger problem or maybe it all falls apart. Sustainability is about the idea that we are going to be able to sustain this solution, this fix. It's going to stay in place. And it's saying that in order for something to stay in place, for something to really work um and be resilient, we need to talk about these three core aspects of it. Okay? So that this, you know, without these three legs, it falls apart. You have to have these three legs of sustainability. So one is economic: um profits are important, profitable farms that keep growers in business. Okay, so that's one because you can't have farmers that are starving. You have to have farmers that are making profit so that they can sustain themselves. Ecological. Ecological is um sometimes it's referred to as ecological, sometimes it's referred to as environmental. Um, the second leg, this is where, for instance, healthy soils, clean water, biodiversity, and climate resilience all come together. Um, again, ecological is to make sure that we are not damaging our soils and our water and biodiversity and that it can withstand the climate changes that um we've been experiencing. Right? So this is a very second important leg. Uh, the third leg, a lot of people don't know about. It's under-researched. It's social. It's not as concrete. It's uh it's got some really interesting characteristics about it. Um, it's the third leg, but it's just as important as the first two. Okay. So remember, all legs are important in sustainability. Otherwise, it falls apart. You don't have sustainability without these. Then you just have a solution that's not going to stay in place. And so social, this is where strong communities, equitable access to resources, and shared knowledge come into place, for example. And this can um there there are a lot of details about this. There's also because this is more of the abstract concept of uh aspect of sustainability, it's also um has different interpretations. Um, it's also um still in the process of being rigidly standardized. What what do you mean by social? Um, but it's it's a very interesting conversation, and we're going to dive into this last leg um, and also how it how it uh what what the dynamic is between all three of these when it comes to generating knowledge.

Okay, so sustainability isn't just about money or ecology alone. Remember, it's the intersection. A truly sustainable practice must earn a living, protect the land, and uplift the people who depend on it. Okay.

So, we have SAR, the program at USDA. They're saying that local voices, they're important. Um, they're saying that farmers should be at the table along with the researcher and educator, and they should together define what is the biggest problem, what does success look like. Um, they should be uh collaborators, co- um co-researchers together. So SAR shows that local voices reshape what counts as valuable data. Okay. Um, farmers' lived experience defines which sustainability metrics matter. So farmers' lived experience is what defines which sustainability metrics matter. Why are we talking about farmers' lived experience? Because we're not talking about um people who and and I'm not saying there are farmers who have obviously they've attained an agriculture degree, but this is not we're what we're talking about because we have those researchers already. We have the researchers that are at the university. We're talking about farmers who have the experience. Okay? So um, whether they come from an agricultural degree or not, we're talking about the the people who have then gone off and farmed themselves. Okay? So they have a lived experience which helps define which sustainability metrics matter um along all three of them. Okay? Along all three legs, you know, what what does it mean to be profitable? What does it mean to um have ecological balance? I mean, what do these metrics look like? Okay. And um in our food aid context, we've been talking about food aid. I want you to think about whose voices and values are driving the data we collect and what might we be missing. So, um, this is this example will teach you how to ask this question when you're doing your own um data analysis project. Think about whose voices and values are driving the data we collect and what might we be missing. So by starting with SAR's three-leg approach and participatory ethos, we can see and because this is this is what participatory research looks like, right? You have um people with a lived experience who are participating in the research. We can see that data isn't neutral. It reflects whoever set whoever set the questions. Okay. So data is not neutral. It's about whoever set the questions. Um, in this context, the SAR context, it's about the farmers, the researchers, and the educators. Okay.

So next, what we're going to do is we're going to dive into a paper that discusses this program. So up until now, these last slides, I was introducing to you um this program SAR, why they exist, and how it's uh it's been a progress, it's been a progress to say farmer knowledge matters, we need to have them at the table, but now we're going to take it to the next level. We're going to have uh we're going to look at a paper from someone called Tanaka and Buffsar. These are two different um authors uh co-authoring this paper, and we're going to look at what how they talk about this. Okay.

So the study that I'm referring to is called "The Role of Southern SAR Projects in Enhancing Quality of Life." Um, the last leg of sustainability sometimes is called quality of life. Others refer to it as social sustainability, and Tanaka and Buffsar are focusing on this last uh leg. They are both sociologists, and so this is their interest. Um, so their focus is how farmers shaped research questions and defined success in sustainable agriculture. Okay, with a focus on quality of life. They want to know how these SAR projects are enhancing quality of life. So again, we know that SAR has flipped the script, right? They're instead of experts telling farmers what to grow, um, they're saying farmers should also co-design the research and generate the knowledge together. Okay, so they are creating that data together. So whose knowledge counts for SAR? Well, they say that farmer's knowledge also counts at the same level, equally. And this is a real-world demo of social constructionism in action. So we're going to these authors talk about social constructionism. Um, and this is what we're going to now focus on. Okay.

So, uh, an example of farmers as co-researchers is going to be things like participatory workshops, let growers pinpoint their own challenges. Okay? So, whether it's soil compaction or fertilizer rates, um, farmers prioritize practical, place-based questions over academic agendas. Okay? So, um, they have a pragmatic approach. They're prioritizing things that matter to them. Place-based questions over academic agendas. An example quote is, "We needed a method that worked on these heavy clay soils in July heat." Okay, so farmers weren't subjects. Remember, farmers were not subjects. They were equal partners in defining what questions matter so that together they could uh create this knowledge. So they are creating this knowledge together. In most projects, the university sets the agenda here. Funding rules force institutions to listen to farmers. That's social constructionism. When knowledge is built together, if you remember, okay, it's built together, um, negotiated together. So institutional assumptions, funding priorities, academic publication can often eclipse farmers' needs. SAR funding opens up the space for non-traditional metrics. Farmers decided which variables to track. Okay, they're deciding what the data is going to be, and they're deciding what gets generated at the end, the value of that. They're saying, this is what's valuable. This is the knowledge that's valuable. And this contrasts traditional yield studies versus farmer-driven questions. Okay, so yield studies is all about how do we how do we make more crops? How do we create more crops? How do we u make sure that we don't lose our crops to this particular pest this year? And how do we ensure that um we can quickly flip over the land and plant even more and that sort of thing. Right? So um, so it's an example that the contrast here is traditional yield studies, right? So that there was a value with this is what type of knowledge that was valued: how much, you know, how can we produce more, how can we produce more food, and instead the value was placed for was placed by farmers with farmer-driven questions. Okay.

So quality of life metrics, again, this is our third leg that we're talking about. Okay. We're talking about the third leg here. We talked about sustainability, but remember, this lecture is now about that third leg. Okay. So SAR is focusing on all three legs of sustainability. This research that I'm talking about from Tanaka and Buffsar, they're focusing on the third leg as sociologists. Okay. So um, the quality of life metrics, also known as social sustainability. These are social indicators that include community cohesion, local decision-making power, and knowledge sharing. Farmers rated success on neighborhood trust and peer-to-peer learning, not just bushels per acre. Okay, so there's trust and bonding, cohesion. The these are um very these are often talked about in community studies. The these are um these values are often talked about in obviously quality of life metrics um, and for uh when people talk about social well-being in general.

So we talked about the focus of this study by Tanaka and Buffsar. They had the question that we are learning about today, which is whose knowledge counts, and looking at SAR reports. Okay. So they looked at the SAR program, which funds projects to farmers, researchers um, and educators, and um, as part of the funding, you then submit your u result, your uh a summary, a summary of your project that was funded by SAR. So SAR has a collection of these reports that they had funded. Okay. So what these actors did, what the actors, the authors did, was to uh look at these reports and look for patterns and trends. Okay. So they're um, again, their question is the reason why we're talking about this is because their question has to do with whose knowledge counts. And this is going to demonstrate to you what we mean by whose knowledge counting and its impact in um the generation of knowledge. Okay.

So SAR is trying to learn about um what matters and how to solve this problem with food and agriculture. Um, they are funding projects that put farmers at the same table as educators and researchers. And um, when they fund these projects to various farmers, uh, they receive back a summary of the report. In that report summary, these authors are looking for some patterns for the type of knowledge that it reflects. Okay. So they looked at about 174 reports, their final or annual report summaries, submitted to the agency's website. They what they did was they went through uh to they were looking at these questions. They raised the questions: Are some topics over or underrepresented? Do topics change over time? Are some topics over or underfunded? And they raised these questions because they were making the connection that uh based on their assumption that research topic affects the type of networks required to carry out a project, and therefore the type of knowledge to be generated from it. Okay. Uh, we're going to talk about this network-knowledge connection in the next slide a bit more. But so again, they're they raise these questions um, and central to the discussion is essentially, again, remember their focus is quality of life or the social sustainability metric, right? The the third leg of sustainability. And so they are trying to understand, does this knowledge, does this uh, does this conversation, information, the data, this information that's being collected, does it exist here? Are we seeing enough conversation about the quality of life, the third leg of sustainability?

So the authors Tanaka and Buffsar are evaluating SAR's funding pattern um by looking at the reports that people have submitted, and these reports tell them who was funded by SAR, and so they're, you know, at the core of their question, this is just reiteration from the previous slide, um, the core of their question is, who's who counts as knowledge? Whose knowledge is this? Right? Um, so there's one layer we've already talked about. We've talked about farmers, right? So um, SAR has uh flipped this um this model of it being top-down, and they're working with ground-up with practical experience by putting farmers at the same table with uh researchers and educators. Right? So there's that one aspect of whose knowledge. But now they're taking a um a closer look, these authors Tanaka and Buffsar, and they're saying, um, someone has to decide who to choose for funding, right? So someone, there's a committee, there's uh, there's a a person at SAR or people at SAR that are deciding who gets funded. And um, and they're sort of they're trying to understand what projects do they find worthy of funding. And because ultimately these projects that they're funding, because they're producing data, because they're taking farmer data and it's turning into knowledge, it's answering the question, whose knowledge counts, right? Because if it's being dictated through, so we already know that farmer knowledge counts. But now they're looking specifically at the three legs of sustainability and they're trying to understand um if there's any sort of pattern in terms of which one of those legs, you know, are these are these projects being funded across the three legs of sustainability? So meaning, if the farmer is interested in um increasing yield and um some ecological uh outcomes, um are these are these seen as more valuable? Are they getting more uh funded or more of these type of studies getting funded than a farmer who's interested in community cohesion and um trust, relationships between he and the vendor, and that sort of thing, which is the the third leg of sustainability. Okay.

So um, so now they're taking, you know, they're looking even closer. They're saying, okay, that's great. SAR has done this great thing about look um talking about sustainability by getting farmers off the table, but can we see if um they are funding according to their intention? SAR's intention is to um is to address sustainability. And so these authors Tanaka and Buffsar are trying to answer the question whether they are achieving that goal that SAR has set out for the program. And so, uh, that was a reiteration that I went through, uh, and further clarification. And now I'm going to talk about actor-network theory. And so, one of the frameworks that they or methodologies that they use is called actor-network theory. The the authors that have written this paper about SAR and um the actor-network theory was um invented by Latour, Bruno Latour. You may have heard about him. He's popular in the science and technology um field. He's an anthropologist, sociologist, philosopher. He's also popular with political science people. So, okay.

So what does Latour say? Latour says that there's uh he created this actor-network theory, and he says that it's important to treat both human and non-human, and by non-human in this example, we're talking about soil sensors, workshop forms, funding rules, as actors in a network that produce knowledge. Okay. So, um, they're all actors, human and non-human, and they are in a network producing knowledge. In SAR, the USDA guidelines, for example, would be an actor. So the USDA guidelines, the grant reviewers, the farmer's tools, and workshop notes all co-produce the knowledge. So again, we're talking about Latour, we're talking about how he defines um producers of knowledge. Okay? And um to map this network, one of the ways that you would analyze something like this is you would map the network to figure out who influenced which questions and data flows. Okay. So we will come back to that um again in our class. Um, but for now, let's talk about ANT. Okay.

So ANT shows that data isn't just recorded. It's co-produced. Right? So we I'm just repeating some stuff here. It's co-produced by a web of people, machines, and rules. So in SAR, farmers, funders, facilitators, they all matter. ANT or ANT teaches us to see every element: people, regulations, machines, data collection tools as an actor in a network that co-produces knowledge. In the SAR case, the network includes farmers, grant guidelines, extension agents, workshop forms, even soil soil probes, like actual tools. Okay. Um, okay. So by treating each of those as actors, we can map who has voice, who gets silenced in the process of making and using data. So the to reiterate what these authors were looking for, they didn't just catalog outcomes, they interrogated which topics got funded and why. They wanted to understand which topics got funded and why. They assume each research topic demands a different constellation of actors, like farmers, extension agents, funding rules, workshop format, soil tests. So the very choice of topic preconfigures who gets to participate. Okay. Um, we're talking about whether they're trying out a new, say, a new herbal fertilizer. And so there, the network would be a little different, right? The the the person who's produced it, the labs that have created this product of fertilizer, um, their scientists that would be part of the network. Okay.

So this is all to give you a really detailed, vivid example of answering the question, whose knowledge counts. Okay. So it's you don't need to remember all this content, but you do need to understand how how uh thinkers, scholars, how they go about the uh answering this question, whose knowledge counts. Okay. So this is one of the ways. So by asking about under or over-representation of topics, they're really asking which community voices or scientific approaches are being prioritized, and which, in effect, are being sidelined. The actor network around each topic dictates what data and whose expertise end up in the final reports. Okay, remember these final reports, they're producing knowledge. And so we want to this is really important. What they're saying is that the actor network around each topic dictates what data and whose expertise end up in the final reports. The author the author's aim is to understand the effectiveness of the Southern SAR research and education program to to achieving its goal. Whose knowledge got counted? Remember this is what they want to know. Whose knowledge got counted? Whose knowledge counts. Um, and they're saying that by looking at these final reports, because this is knowledge that they're producing, because these are reports that as co-researchers, co-scientists, these farmers have uh produced, right? And um, and that's great. The farmers have a seat at the table. But now they're these authors are um taking an even closer look by saying, "Yeah, but which topics are is there a pattern in terms of which topics are being chosen by the funders, by the grant reviewers, and that sort of thing? And um, does that reflect the funders and the grant reviewers' uh values? Are they um somehow, is there a bias there, essentially?" Okay. Even though there's this really um this program which is very progressive and that it has flipped the this model of bringing farmers to the table, um, there still may be some biases in the way that they are making these funding decisions. Okay.

So here's some more on Latour. We're talking about Latour because in the well, he's first of all, he's very valuable in the science and technology um area. He was a philosopher, a sociologist, and an anthropologist, and and also political science folks um also um found him very his work very significant for for the field. So Latour's key insight is that science doesn't just produce knowledge, it co-produces the world we live in. And the actor-network theory, which is the uh method for analysis that these authors who are looking at SAR, who are evaluating SAR's funding pattern, what this what he says about this is that every upgrade, so think about not just every new technology, but even an upgrade, every upgrade, so new deed, a new machine. It rearranges the network. So we talked about the network, right? The network of actors. Well, it rearranges the network. It reshapes social rules, value metrics, and who counts as an expert. So the connection to whose knowledge counts? Well, what will happen is each technological shift will sideline some voices, right? So for example, traditional breeders, local seed savers, and they will elevate others, right? Agronomists and equipment vendors, right? So if there's some new invention, um, they're these old voices where they are locally saving seeds might get sidelined by a new seedless variety that you buy directly from a company and you never have to save seeds again, but you have to pay the company forever. Um, and so essentially there's there's a lot of critique about this, right? But so um, what happens is with Latour's key insight, anytime there is um because there's this network that creates the knowledge, anytime there's a shift in technology, voices will come, s voices will um be bluffed out, and others will become more dominant. So something I want to say here is these scientific advancements. Something to understand is that these scientific advancements don't just emerge in a vacuum. They reshape what we call, for instance, say agriculture, in terms of who takes part in it, which practices count as good farming, um, how we define something, how we measure something as success, as progress, all these things become reshaped with scientific advancements. And once a technology or finding is out there, its meaning and impact depend on who adopts it and how they adopt it. Um, so another example to give you, sustainability is a reaction. Okay, you can think about sustainability as a reaction. Sustainability, they're not just technical tweaks. They reflect reflect social actors who push back against systems that prioritize profit over community or or ecosystem health. Okay. So there was a time where all that mattered was uh profits. Um, and then we realized that it was damaging the environment and costing us a lot of a lot a lot of money um and more than just money. And so then we started to um incorporate ecosystem health as part of the as part of the uh indicator for whether we were making progress with some approach. And um, and then came about the third leg to say, wait a minute, you also need to uh include in this equation because it's not enough just to think about economy um and material uh environment, but there's also uh social well-being, mental well-being that matters a lot. Um, labor and wages and all these other things also matter, right? So this was a third leg that was created, and so it was a reaction. So these voices were being left out. These voices were being left out initially with the onset of new technologies. Okay. Um, and you can think about um the industrial er era where we factories, factories went up, and things were great. We were able to uh produce things faster and at a more massive volume, and but there were costs to this, right? There were a lot of voices that were being left out, and until eventually this concept of sustainability came about, and people said, "Wait a minute, we can't just be focused on profits. We we have to turn our attention to these other issues that are cropping up and not just cropping up, but that are that are that have become very salient and that are costing lives and creating dangerous conditions." Okay. So this is these are some examples of what we mean by when there's a technology, how voices get sidelined and others become more dominant.

By the end of this slide deck, you should now be able to describe how participatory research shifts the question from "What do experts think matters?" to "Which stakeholder perspectives get recorded and why?" Explain how co-design methods ensure local or lay expertise is treated as valid data alongside formal scientific protocols. Analyze how funding and institutional rules can amplify certain voices over others in defining research agendas. Illustrate how any complex concept like sustainability is shaped by multiple expert and stakeholder knowledges. And lastly, map the network of actors, human and non-human, that co-produce a given body of knowledge. And for the next slide deck, you will watch part two of Whose Data Counts.