10 episodes
How do you know? Christie Bahlai
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- Science
It’s ironic that in an era of big data, truth sometimes seems more elusive than ever. To make better choices about how to manage our lives, our work, and our environment, we need to use the best possible information to guide us. But even with great data, humans don’t always make great choices- we misinterpret, we oversimplify, we fail to see fallacies in logic or flaws in the data itself- and even our most rational examinations of the numbers are fundamentally human, shaped by culture, prior experience, and our internal biases.In this podcast, we explore the process of how data becomes information, information becomes knowledge and knowledge becomes belief- and how, in turn, belief shapes the way we take and interpret data. From young learners to practicing scientists, the ways we incorporate information into our worldview is affected by our experiences. We combine social and data science perspectives with the study of how humans learn, in order to examine not just what we know, but how we know it.
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An absolutely irreproducible conversation with Nicole Nelson
What happens when we can't reproduce our work- or someone else's? What does it mean about the science- and ourselves?Today on the podcast, we're talking to Nicole C. Nelson, an associate professor in the Department of Medical History and Bioethics at the University of Wisconsin, Madison, and an affiliate of the Holtz Center of Sciences and Technology Studies. Nicole's work examines scientist's assumptions about the natural world and how these assumptions shape scientific practice. ...
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A balanced diet of machine learning education with Carrie Diaz Eaton
Welcome back to season 2 of HDYK! Today we're talking about how our positionality and the assumptions we make affect our approaches in science, but also thinking about how we turn that positionality into a strength by incorporating diverse viewpoints. We're starting this season with a great conversation with Dr. Carrie Diaz Eaton. Carrie is a mathematician and associate professor of digital and computational studies at Bates College. She co-founded QUBES, which stands for Quantita...
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HDYK Episode 8: Eminent ladybugologists and zombie ideas with Kaitlin Stack Whitney and Sara Hermann
What happens to public understanding when science communication goes wrong? Experts go on the media to talk about their work, and somehow, something doesn’t connect. This misinterpretation gains steam, and soon it becomes an outright conspiracy, used to manipulate, polarize and undermine one political agenda or reinforce another. The misunderstanding becomes the message- chances to communicate new findings get lost as we get pulled into conversations trying to debunk rumors and myths. Today, ...
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HDYK Episode 7: Data spaces, data places- With Deondre Smiles
Science often treats data and information as a resource that can be extracted. Like colonists coming to conquer new lands, scientists trained in the dominant paradigm often frame data-driven discovery with the same language on the frontier myth used on lands and people. But where is this data coming from- and who is impacted by its extraction? What if, instead of extraction, we thought about data sovereignty? Today on the podcast, we’re Talking to Dr. Deondre Smiles. Deondre Smiles is a new A...
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HDYK Episode 6: We've Got History- With Sarah Qidwai
And now, as Monty Python would say, for something completely different. Or is it? Is it data, is it science? The humanities produce knowledge. Yet, they’re not generally considered part of STEMM. So, what about humanities research on STEMM? What does this look like? Where does it fit in? As we continue to explore the human side of science, in this episode we consider the history of science, and also what it is like to be an early career researcher now navigating the Academy and transiti...
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HDYK Episode 5: Who’s asking? With Leon Walls
Sometimes, when researchers ask what seems to be a simple question, they get a lot of different answers. When they look closer, they see that their simple question may be made up of many questions, and how you ask them depends on how-and who- you measure. To make things more complicated, who’s asking the question- and who’s answering- can matter a lot. This becomes a huge problem when researchers use ‘easy’ samples for their work. Today on How Do You Know, we’re talking to Dr. Leon Walls, an ...