Raw Data is an Oxymoron

By James Mussell. 2014

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Citations in the corpus

Critical data studies: An introduction (2016, background): “, 2016; Striphas, 2015), and researchers invoke data in the name of scientific objectivity while often ignoring that data are never raw but always ‘‘cooked’’ (Gitelman, 2013).”
On the genealogy of machine learning datasets: A critical history of ImageNet (2021, background): “Data as a concept or object has been written about at length within science and technology studies (e.g. Gitelman, 2013).”
Disembodied Machine Learning: On the Illusion of Objectivity in NLP (2021, background): “Datasets, following Haraway (1988), can be understood as a form of knowledge that does not simply exist but is produced (Gitelman 2013) through embodied experiences.”
Gender Bias in Machine Translation (2021, background): “Treating data as an abstract, monolithic entity (Gitelman, 2013) – or relying on “overly broad/overloaded terms like training data bias”8 (Suresh and Guttag, 2019) – does not encourage”
Artificial intelligence and illusions of understanding in scientific research (2024, unknown): “Curating training sets and designing a training regime involves dozens or even hundreds of judgements that can imbue algorithms with the values of their creators 47,137 .”
Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments (2016, unknown): “The turn to visualization in education is part of a wider trend where data are increasingly visualized and mobilized graphically in a ‘ cascade of representations ’ (Gitelman and Jackson 2013, 12).”
European energy poverty metrics: Scales, prospects and limits (2020, unknown): “Data and metadata are analysed in STS, along these lines, as contingent socio-technical effects [29,112].”
Big Data in food and agriculture (2016, background): “…of the production and use of Big Data is precisely what critical data studies scholars do when they highlight Big Data as actively framed and interpreted by people, in particular social and technical contexts (Bowker, 2005; Edwards, 2010; Gitelman and Jackson, 2013; Hacking, 1992; Porter, 1995).”
Where are human subjects in Big Data research? The emerging ethics divide (2016, background): “Critical data studies have routinely demonstrated that it is deeply mistaken to treat research data as neutral and raw (see, for example, Bowker, 2005; Gitelman, 2013).”
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021, unknown): “Central to the practice of science—and as such, to machine learning—is data, the vague notion of “raw” facts, text, numbers, or other inputs that can be organized via analysis into meaningful information [39, 46, 99, 119].”
Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites (2015, background): “…have called attention to the decisions that go into what data are generated and collected in the first place (Baym 2013; Bowker 2005; Crawford 2013; gitelman 2013; tufekci 2013), different understandings of the notion of big data (Neff 2013), thorny issues of privacy (Lazer et al. 2009; Schneier…”
Youth Perspectives on Critical Data Literacies (2017, background): “Some scholars have framed this by suggesting that “raw data is an oxymoron” [25] or that raw data needs to be “cooked with care” [3].”
Critiquing Big Data: Politics, Ethics, Epistemology (2014, background): “Raw Data Is an Oxymoron , a collection edited by Lisa Gitelman (2013), takes up this question by examining “the imagination of data” across various disciplines, eras, and media.”
Working and organizing in the age of the learning algorithm (2018, background): “Considering data to be raw is an “oxymoron” (Bowker, 2005; Gitelman, 2013), meaning that data is never pure, raw, or objective.”
Big Data Surveillance: The Case of Policing (2017, background): “…on mechanical objectivity; see also Espeland and Vannebo 2007; Hacking 1990), new analytic platforms and techniques are deployed in preexisting organizational contexts (Barley 1986, 1996; Kling 1991) and embody the purposes of their crea-tors (boyd and Crawford 2012; Gitelman 2013; Kitchin 2014).”
Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject (2018, background): “That is the underlying reason why there cannot be raw data (Gitelman 2013): because what is “given” must first be configured for “capture” (Kitchin and Dodge 2011).”
Impediment to insight to innovation: understanding data assemblages through the breakdown–repair process (2016, background): “Every disciplinary institution and body of knowledge has ‘ its own norms and standards for the imagination of data, ’ and ‘ different data sets harbor the interpretive structures of their own imagining ’ (Gitelman & Jackson, 2013, p. 3).”