{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:28Z","timestamp":1777455808030,"version":"3.51.4"},"reference-count":39,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005538","name":"Annenberg School for Communication and Journalism, University of South California","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005538","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>There is a gap in existing critical scholarship that engages with the ways in which current \u201cmachine listening\u201d or voice analytics\/biometric systems intersect with the technical specificities of machine learning. This article examines the sociotechnical assemblage of machine learning techniques, practices, and cultures that underlie these technologies. After engaging with various practitioners working in companies that develop machine listening systems, ranging from CEOs, machine learning engineers, data scientists, and business analysts, among others, I bring attention to the centrality of \u201clearnability\u201d as a malleable conceptual framework that bends according to various \u201cground-truthing\u201d practices in formalizing certain listening-based prediction tasks for machine learning. In response, I introduce a process I call Ground Truth Tracings to examine the various ontological translations that occur in training a machine to \u201clearn to listen.\u201d Ultimately, by further examining this notion of learnability through the aperture of power, I take insights acquired through my fieldwork in the machine listening industry and propose a strategically reductive heuristic through which the epistemological and ethical soundness of machine learning, writ large, can be contemplated.<\/jats:p>","DOI":"10.1177\/20539517221146122","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T01:17:36Z","timestamp":1673227056000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":29,"title":["Ground truth tracings (GTT): On the epistemic limits of machine learning"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4919-1832","authenticated-orcid":false,"given":"Edward B","family":"Kang","sequence":"first","affiliation":[{"name":"University of Southern California, Annenberg School for Communication and Journalism, Los Angeles, CA, USA"}]}],"member":"179","published-online":{"date-parts":[[2023,1,8]]},"reference":[{"key":"bibr1-20539517221146122","doi-asserted-by":"publisher","DOI":"10.1177\/2053951715622512"},{"key":"bibr2-20539517221146122","doi-asserted-by":"publisher","DOI":"10.1007\/s11186-020-09411-3"},{"key":"bibr3-20539517221146122","volume-title":"The atlas of AI: Power, politics and the planetary costs of artificial intelligence","author":"Crawford K","year":"2021"},{"key":"bibr4-20539517221146122","unstructured":"Dal Pino (2014) Do you know the standard of care? 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