我要吃瓜

Citation

Thompson T (2023) Researching with, on, in and through the postdigital: Accounting for more-than-humanness. In: Jandric P, MacKenzie A & Knox J (eds.) Postdigital research: Genealogies, challenges, and future perspectives. 1 ed. Postdigital Science and Education. Cham, Switzerland: Springer, pp. 231-247. https://doi.org/10.1007/978-3-031-31299-1_13

Abstract
This chapter engages with the messiness of researching with, on, in, and through the postdigital to explore what this research might entail methodologically and theoretically. I draw on more-than-human sensibilities to untangle three knots. First, how can more nuanced attuning to the co-mingling of humans and technologies enable researchers to move beyond persistent binaries and can such binaries be generative? Second, how do researchers and theorists attune to their own more-than-humanness? Third, how does this attuning contribute to the value, credibility, quality, and politics of postdigital inspired research and accounts of these inquiries? I introduce data from a research project in which more-than-human research sensibilities and methods (e.g., technography, interviewing objects, situated logging, and descriptive anecdoting) were employed to enable participants in this study to notice their own more-than-humanness as they attuned to the digital mediation of their everyday work practices: specifically, the increasing use of automated and assisted decision-making (AADM) in professional work. Data foregrounds several tensions of postdigital research including how these practitioner-researchers attempted to erase, work across, and reify human-digital binaries. Ontological wrestling unfolded as they noticed and questioned their own more-than-humanness to varying degrees: who-what they become with the AI and advanced data systems of their research inquiry. A re-thinking of human-digital practices in more speculative and provisional ways is evident, albeit often layered with caveats.

Keywords
Postdigital; More-than-human-Research; Feminist materialist methodologies; Professional work-learning; Artificial Intelligence