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Accepted Paper:
Towards an idiographic data practice for computational anthropology
Anders Kristian Munk
(Technical University of Denmark)
In this paper, I lay out the contours of an idiographic data practice for computational anthropology. Expanding on a recent engagement with AI-related conrtroversies on Wikipedia I try to make clear how coding and computation in an online fieldwork setting can be distinctively non-nomothetic.
Paper long abstract:
In this paper, I lay out the contours of what I tentatively call an idiographic data practice for computational anthropology. Expanding on a recent engagement with AI-related conrtroversies on Wikipedia I try to make clear how coding and computation in an online fieldwork setting can take on a flavour that is distinctively different from what is typically associated with nomothetic computational social science. When deployed as part of a descriptive and explorative endeavour that rests on ethnographic tenets like the ability to discover new questions from the setting and reformating research problems in the field, Python libraries for scraping data, interacting with APIs, and conducting natural language processing or other machine learning tasks cannot be put to their best if they are understood as instruments in an essentially quantativist and predictive toolbox. Questions about explainability, efficiency, or reliability fade into the background in favour of questions about adaptability and how to tailor uniquely adequate solutions for empirical situations that are constantly changing. Based on examples from my own work with a large Wikipedia corpus I therefore propose a set of principles for what an idiographic data practice in computational anthropology could aspire to and be judged on.