Memory Practices in the Sciences

By Martin Guha. 2007

514 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

The conundrum of police officer-involved homicides: Counter-data in Los Angeles County (2016, methodology): “This literature argues that at any given moment, data are only snapshots that have been composed through the modeling and ‘‘cooking’’ that happens in the algorithmic process of data capture (Bowker, 2007).”
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021, unknown): “Bowker examined how the data considered most interesting and classifiable shapes how data is then stored, and how data deemed uninteresting or difficult to classify can become underspecified or entirely lost in larger data catalogues [13].”
Information, Divergence and Risk for Binary Experiments (2009, background): “Geology Finally, Lyell’s Principles of Geology (Lyell, 1830) was a watershed in Geology’s history (Bowker, 2005); prior work is pre-historical.”
Metaphors of Big Data (2014, background): “The understanding of data as any stored piece of digital information “given” by computational storage marks a departure from previous understandings, a shift with implications for established memory practices in science and scholarship (Bowker, 2005).”
Towards Critical Data Studies: Charting and Unpacking Data Assemblages and Their Work (2014, background): “As Gitelman and Jackson (2013: 2, following Bowker 2005) put it, “raw data is an oxymoron”; “data are always already ‘cooked’.””
Latent life: Concepts and practices of human tissue preservation in the International Biological Program (2013, background): “3 Attention to cryopreservation can also contribute new insights into long-standing efforts to address the management of stored biomaterials across the life sciences (Bowker, 2005; Fortun, 2008; Pálsson, 2007; Parry, 2004b; Tutton and Corrigan, 2004; Weir and Olick, 2004).”
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).”
Representation and communication: challenges in interpreting large social media datasets (2013, background): “A more subtle approach is taken by Bowker [4] who writes in detail about how the authorship of particular representations is embroiled in their use.”
Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments (2016, unknown): “As Bowker (2005, 30) notes, the governmental operations of the state and the functional logics of databases are symmetrical, and ‘ a good citizen of the modern state is a citizen who can well be counted – along numerous dimensions, on demand ’ .”
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).”
Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites (2015, background): “Scholars 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;…”
The ethics of smart cities and urban science (2016, background): “In other words, data are never raw, but always already cooked [75].”
If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology (2013, background): “science, but only recently are being applied to the study of data practices and data sharing [28,39,40].”
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].”
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.”
Indigenous body parts, mutating temporalities, and the half-lives of postcolonial technoscience (2013, background): “It draws attention to how multiple contexts and timescales – evolutionary, human, and microbial to name just a few – are torqued as well as imbricated through technologies of freezing and practices of thawing (Adams et al., 2009; Asdal, 2012; Bowker, 2005; Schrader, 2010).”