It is interesting to reflect, for a moment, on the lengths I have gone to learn, understand and develop natural language processing methods/tools. After all, I didn't start out with the intention of developing a specific expertise in NLP, rather I always considered it a means to an end. The end being the ability to ask and answer various research questions, and derive insight into some social or ecological phenomena of interest.
In my dissertation research, one of my projects focused on measuring coherence of ideological arguments related to agri-food movements (i.e. local food, slow food, sustainable agriculture, etc.). I developed a modified form of discourse analysis to identify and connect statements among food movement actors to better determine the implicit connections between what people say they stand for, and the ways they act (or don't act) to materialize their ethical or moral arguments. Looking back, this is and was a rather amazing NLP task. I didn't realize it at the time, but this would help launch an almost obsessive relationship with language processing.
Chapter 2 of the dissertation can be access here: Rhetoric and Reality of Social Equity in Agri-Food Movements.
Fast forward to my post-doctoral research and again, I found myself processing and connecting language to represent cognitive structures or frames about disaster events. The goal was to determine if specific social learning activities altered these cognitive frames about the preceding disaster event and whether these frames led to greater community resilience via a shared or collective frame about the event and ways to respond in the future.
Through these experiences and with a growing number of new research questions driving my intellectual curiosity, NLP as a discipline in human-machine learning has taken on a much more central role to my overall research trajectory.
In my dissertation research, one of my projects focused on measuring coherence of ideological arguments related to agri-food movements (i.e. local food, slow food, sustainable agriculture, etc.). I developed a modified form of discourse analysis to identify and connect statements among food movement actors to better determine the implicit connections between what people say they stand for, and the ways they act (or don't act) to materialize their ethical or moral arguments. Looking back, this is and was a rather amazing NLP task. I didn't realize it at the time, but this would help launch an almost obsessive relationship with language processing.
Chapter 2 of the dissertation can be access here: Rhetoric and Reality of Social Equity in Agri-Food Movements.
AbstractIncreasing sustainable food consumption is essential to reducing the environmental and social impacts of our current industrial agri-food system. However, public perceptions of sustainability remains contested, fluid, and in some cases incomplete. Groups locked in the fight over the future of our food systems advocate varying perspectives of sustainability that reflect their interests and ideologies. This may complicate people's ability to make adequately informed purchasing decisions, undermining the efficacy of a sustainable consumerism. A sustainable agri-food system is often framed with an emphasis on environmental health and economic viability for farmers. Yet, social equity represents the "third pillar" of sustainability, and is an important aspect of sustainable food consumption. This study analyzes the degree to which social equity is included among efforts to shape public understanding of sustainable food and farming systems. In particular, I analyze websites from groups advocating for alternative and conventional agri-food systems in the United States. This study shows that social equity is a difficult concept to communicate. This difficulty has the potential for minimizing, and obfuscating the role of equity in consumer’s food purchasing decisions. The implication is an environmental and economically sustainable food system that remains inequitable. Results also indicate that advocates of alternative agri-food systems need to be more inclusive of social equity in order to maintain a distinction from conventional actors who are adopting the language of sustainability to capitalize on consumer demand.
Fast forward to my post-doctoral research and again, I found myself processing and connecting language to represent cognitive structures or frames about disaster events. The goal was to determine if specific social learning activities altered these cognitive frames about the preceding disaster event and whether these frames led to greater community resilience via a shared or collective frame about the event and ways to respond in the future.
Smith, J. G., DuBois, B., & Krasny, M. E. 2015. Framing Resilience through Social Learning: Impacts of Environmental Stewardship on Youth in Post-disturbance Communities. Sustainability Science, 1-13. DOI: 10.1007/s11625-015-0348-yIn both examples, I was less interested in the mechanics of NLP except in as much as I could ensure I was using the correct methods to generate results relevant to answering my research questions. However, the deeper I dive, the more relevant those mechanics have become, and this has meant diving into the mathematical formalisms of describing language models and semantic representations of data. And while I can honestly say I'm a poor student of arithmetic, I have come to find a home in set theory, abstract algebra and the formalism of deductive methods. Besides, computers are great at arithmetic!
Through these experiences and with a growing number of new research questions driving my intellectual curiosity, NLP as a discipline in human-machine learning has taken on a much more central role to my overall research trajectory.
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