Pub Date : 2023-01-01DOI: 10.1177/20539517221149106
Adam S. Miner, Sheridan A Stewart, M. Halley, Laura K. Nelson, Eleni Linos
Differences between computationally generated and human-generated themes in unstructured text are important to understand yet difficult to assess formally. In this study, we bridge these approaches through two contributions. First, we formally compare a primarily computational approach, topic modeling, to a primarily human-driven approach, qualitative thematic coding, in an impactful context: physician mothers’ experience of workplace discrimination. Second, we compare our chosen topic model to a principled alternative topic model to make explicit study design decisions meriting consideration in future research. By formally contrasting computationally generated (i.e. topic modeling) and human-generated (i.e. thematic coding) knowledge, we shed light on issues of interest to several audiences, notably computational social scientists who wish to understand study design tradeoffs, and qualitative researchers who may wish to leverage computational methods to improve the speed and reproducibility of labor-intensive coding. Although useful in other domains, we highlight the value of fast, reproducible methods to better understand experiences of workplace discrimination.
{"title":"Formally comparing topic models and human-generated qualitative coding of physician mothers’ experiences of workplace discrimination","authors":"Adam S. Miner, Sheridan A Stewart, M. Halley, Laura K. Nelson, Eleni Linos","doi":"10.1177/20539517221149106","DOIUrl":"https://doi.org/10.1177/20539517221149106","url":null,"abstract":"Differences between computationally generated and human-generated themes in unstructured text are important to understand yet difficult to assess formally. In this study, we bridge these approaches through two contributions. First, we formally compare a primarily computational approach, topic modeling, to a primarily human-driven approach, qualitative thematic coding, in an impactful context: physician mothers’ experience of workplace discrimination. Second, we compare our chosen topic model to a principled alternative topic model to make explicit study design decisions meriting consideration in future research. By formally contrasting computationally generated (i.e. topic modeling) and human-generated (i.e. thematic coding) knowledge, we shed light on issues of interest to several audiences, notably computational social scientists who wish to understand study design tradeoffs, and qualitative researchers who may wish to leverage computational methods to improve the speed and reproducibility of labor-intensive coding. Although useful in other domains, we highlight the value of fast, reproducible methods to better understand experiences of workplace discrimination.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44128155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517231171058
Renée Ridgway
Google dominates around 92% of the search market worldwide (as of November 2022), with most of its revenue derived from search advertising. However, Google's hegemony over search and the resulting implications are not necessarily accidental, arbitrary or (un)intentional. This article revisits Brin and Page's original paper, drawing on six of their key innovations, concerns and design choices (counting citations or backlinks, trusted user, advertising, personalization, usage data, smart algorithms) to explain the evolution of Google's hypertext search engine technologies through ‘moments of contingency’, which led to corporate lock-ins. Underpinned by analyses of patents, statements and secondary sources, it elucidates how early Google considerations and certain affordances not only came to shape the web (backlinks, trusted user, advertising) but subsequently facilitated contemporary surveillance capitalism. Building upon Zuboff's ‘Big Other’, it describes the ways in which Google as an infrastructure is intertwined with Big Data's platformization and the ad infinitum collection of usage data, beyond just personalization. This extraction and refinement of usage data as ‘behavioural surplus’ results in ‘deleterious consequences’: a ‘habit of automaticity,’ which shapes the trusted user through ‘ubiquitous googling’ and smart algorithms, whilst simultaneously generating prediction products for surveillance capitalism. Advancing Latour's ‘predicting the path’ of technological innovation, this cause-and-effect story contributes a new taxonomy of Google sociotechnical affordances to critical STS, media history and web search literature.
{"title":"Deleterious consequences: How Google's original sociotechnical affordances ultimately shaped ‘trusted users’ in surveillance capitalism","authors":"Renée Ridgway","doi":"10.1177/20539517231171058","DOIUrl":"https://doi.org/10.1177/20539517231171058","url":null,"abstract":"Google dominates around 92% of the search market worldwide (as of November 2022), with most of its revenue derived from search advertising. However, Google's hegemony over search and the resulting implications are not necessarily accidental, arbitrary or (un)intentional. This article revisits Brin and Page's original paper, drawing on six of their key innovations, concerns and design choices (counting citations or backlinks, trusted user, advertising, personalization, usage data, smart algorithms) to explain the evolution of Google's hypertext search engine technologies through ‘moments of contingency’, which led to corporate lock-ins. Underpinned by analyses of patents, statements and secondary sources, it elucidates how early Google considerations and certain affordances not only came to shape the web (backlinks, trusted user, advertising) but subsequently facilitated contemporary surveillance capitalism. Building upon Zuboff's ‘Big Other’, it describes the ways in which Google as an infrastructure is intertwined with Big Data's platformization and the ad infinitum collection of usage data, beyond just personalization. This extraction and refinement of usage data as ‘behavioural surplus’ results in ‘deleterious consequences’: a ‘habit of automaticity,’ which shapes the trusted user through ‘ubiquitous googling’ and smart algorithms, whilst simultaneously generating prediction products for surveillance capitalism. Advancing Latour's ‘predicting the path’ of technological innovation, this cause-and-effect story contributes a new taxonomy of Google sociotechnical affordances to critical STS, media history and web search literature.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41625322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517231153806
Inga Luchs, C. Apprich, M. Broersma
Machine learning (ML) algorithms are still a novel research object in the field of media studies. While existing research focuses on concrete software on the one hand and the socio-economic context of the development and use of these systems on the other, this paper studies online ML courses as a research object that has received little attention so far. By pursuing a walkthrough and critical discourse analysis of Google's Machine Learning Crash Course and IBM's introductory course to Machine Learning with Python, we not only shed light on the technical knowledge, assumptions, and dominant infrastructures of ML as a field of practice, but also on the economic interests of the companies providing the courses. We demonstrate how the online courses further support Google and IBM to consolidate and even expand their position of power by recruiting new AI talent and by securing their infrastructures and models to become the dominant ones. Further, we show how the companies not only influence greatly how ML is represented, but also how these representations in turn influence and direct current ML research and development, as well as the societal effects of their products. Here, they boast an image of fair and democratic artificial intelligence, which stands in stark contrast to the ubiquity of their corporate products and the advertised directives of efficiency and performativity the companies strive for. This underlines the need for alternative infrastructures and perspectives.
{"title":"Learning machine learning: On the political economy of big tech's online AI courses","authors":"Inga Luchs, C. Apprich, M. Broersma","doi":"10.1177/20539517231153806","DOIUrl":"https://doi.org/10.1177/20539517231153806","url":null,"abstract":"Machine learning (ML) algorithms are still a novel research object in the field of media studies. While existing research focuses on concrete software on the one hand and the socio-economic context of the development and use of these systems on the other, this paper studies online ML courses as a research object that has received little attention so far. By pursuing a walkthrough and critical discourse analysis of Google's Machine Learning Crash Course and IBM's introductory course to Machine Learning with Python, we not only shed light on the technical knowledge, assumptions, and dominant infrastructures of ML as a field of practice, but also on the economic interests of the companies providing the courses. We demonstrate how the online courses further support Google and IBM to consolidate and even expand their position of power by recruiting new AI talent and by securing their infrastructures and models to become the dominant ones. Further, we show how the companies not only influence greatly how ML is represented, but also how these representations in turn influence and direct current ML research and development, as well as the societal effects of their products. Here, they boast an image of fair and democratic artificial intelligence, which stands in stark contrast to the ubiquity of their corporate products and the advertised directives of efficiency and performativity the companies strive for. This underlines the need for alternative infrastructures and perspectives.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41796643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517231166887
Louise Amoore, Alexander Campolo, Benjamin N. Jacobsen, Ludovico Rella
Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.
{"title":"Machine learning, meaning making: On reading computer science texts","authors":"Louise Amoore, Alexander Campolo, Benjamin N. Jacobsen, Ludovico Rella","doi":"10.1177/20539517231166887","DOIUrl":"https://doi.org/10.1177/20539517231166887","url":null,"abstract":"Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48636165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517231184891
G. Ottinger, K. Bronson, D. Nafus
Critiques of data colonialism and surveillance capitalism focus on data collected from online behavior. We propose that analytical concepts from these critiques—namely, regimes of value and patterns of alienation and attunement—could be applied more widely to better understand the threats that datafication poses to equity and democracy in the social and environmental realms. Regimes of value, which include the institutions and technologies that make data meaningful and render them selectively available for appropriation, are relevant both to for-profit companies’ data practices and to states’ participation in the datafication of the environment; examining regimes of value raises questions about how data are exploited and how they are neglected. Patterns of alienation associated with datafication include the potential for alienation from the environment; however, at least in some value regimes, alienation may be accompanied by possibilities for attunement to natural and social phenomena that might otherwise have escaped notice.
{"title":"Clicks and particulates: Value, alienation, and attunement as unifying themes in big data studies","authors":"G. Ottinger, K. Bronson, D. Nafus","doi":"10.1177/20539517231184891","DOIUrl":"https://doi.org/10.1177/20539517231184891","url":null,"abstract":"Critiques of data colonialism and surveillance capitalism focus on data collected from online behavior. We propose that analytical concepts from these critiques—namely, regimes of value and patterns of alienation and attunement—could be applied more widely to better understand the threats that datafication poses to equity and democracy in the social and environmental realms. Regimes of value, which include the institutions and technologies that make data meaningful and render them selectively available for appropriation, are relevant both to for-profit companies’ data practices and to states’ participation in the datafication of the environment; examining regimes of value raises questions about how data are exploited and how they are neglected. Patterns of alienation associated with datafication include the potential for alienation from the environment; however, at least in some value regimes, alienation may be accompanied by possibilities for attunement to natural and social phenomena that might otherwise have escaped notice.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46584764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517221135176
S. Masiero
Digital identity systems are usually viewed as datafiers of existing populations. Yet a platform view finds limited space in the digital identity discourse, with the result that the platform features of digital identity systems are not seen in relation to their surveillance outcomes. In this commentary I illuminate how the core platform properties of digital identity systems afford the undue surveillance of vulnerable groups, leading users into the binary condition of either registering and being profiled, or giving up essential benefits from providers of development programmes. By doing so I contest the “dark side” narrative often applied to digital identity, arguing that, rather than just a side, it is the very inner matter of digital identity platforms that enables surveillance outcomes.
{"title":"Digital identity as platform-mediated surveillance","authors":"S. Masiero","doi":"10.1177/20539517221135176","DOIUrl":"https://doi.org/10.1177/20539517221135176","url":null,"abstract":"Digital identity systems are usually viewed as datafiers of existing populations. Yet a platform view finds limited space in the digital identity discourse, with the result that the platform features of digital identity systems are not seen in relation to their surveillance outcomes. In this commentary I illuminate how the core platform properties of digital identity systems afford the undue surveillance of vulnerable groups, leading users into the binary condition of either registering and being profiled, or giving up essential benefits from providers of development programmes. By doing so I contest the “dark side” narrative often applied to digital identity, arguing that, rather than just a side, it is the very inner matter of digital identity platforms that enables surveillance outcomes.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43817463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517231156518
Rosie Graham
What questions should we ask of Google’s Autocomplete suggestions? This article highlights some of the key ethical issues raised by Google’s automated suggestion tool that provides potential queries below a user’s search box. Much of the discourse surrounding Google’s suggestions has been framed through legal cases in which complex issues can become distilled into black-and-white questions of the law. For example, do Google have to remove a particular suggestion and do they have to pay a settlement for damages? This commentary argues that shaping this discourse along primarily legal lines obscures many of these other moral dimensions raised by Google Autocomplete. Building from existing typologies, this commentary first outlines the legal discourse before exploring five additional ethical challenges, each framed around a particular moral question in which all users have a stake. Written in the form of a commentary, the purpose of this article is not to conclusively answer the ethical questions raised, but rather to give an account of why these particular questions are worth debating. Autocomplete’s suggestions are not simply a mirror of what users are typing into Google’s search bar. Google’s official statement is that “Autocomplete is a time-saving but complex feature. It doesn’t simply display the most common queries on a given topic” but “also predict[s] individual words and phrases that are based on both real searches as well as word patterns found across the web” (Google, 2022). Both its underlying methods and associated terminology have changed throughout time, shifting between providing completions, suggestions, and predictions. In doing so, the grounds for potential critique are ever-changing, which means that Google’s approach to Autocomplete deserves significant scrutiny.
{"title":"The ethical dimensions of Google autocomplete","authors":"Rosie Graham","doi":"10.1177/20539517231156518","DOIUrl":"https://doi.org/10.1177/20539517231156518","url":null,"abstract":"What questions should we ask of Google’s Autocomplete suggestions? This article highlights some of the key ethical issues raised by Google’s automated suggestion tool that provides potential queries below a user’s search box. Much of the discourse surrounding Google’s suggestions has been framed through legal cases in which complex issues can become distilled into black-and-white questions of the law. For example, do Google have to remove a particular suggestion and do they have to pay a settlement for damages? This commentary argues that shaping this discourse along primarily legal lines obscures many of these other moral dimensions raised by Google Autocomplete. Building from existing typologies, this commentary first outlines the legal discourse before exploring five additional ethical challenges, each framed around a particular moral question in which all users have a stake. Written in the form of a commentary, the purpose of this article is not to conclusively answer the ethical questions raised, but rather to give an account of why these particular questions are worth debating. Autocomplete’s suggestions are not simply a mirror of what users are typing into Google’s search bar. Google’s official statement is that “Autocomplete is a time-saving but complex feature. It doesn’t simply display the most common queries on a given topic” but “also predict[s] individual words and phrases that are based on both real searches as well as word patterns found across the web” (Google, 2022). Both its underlying methods and associated terminology have changed throughout time, shifting between providing completions, suggestions, and predictions. In doing so, the grounds for potential critique are ever-changing, which means that Google’s approach to Autocomplete deserves significant scrutiny.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47412208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517231171052
L. Rainey, J. Lutomski, M. Broeders
FAIR data, that is, Findable, Accessible, Interoperable, and Reusable data, and Big Data intersect across issues related to data storage, access, and processing. The solution-oriented FAIR principles serve an integral role in improving Big Data; yet to date, the implementation of FAIR in multiple sectors has been fragmented. We conducted an exploratory analysis to identify incentives and barriers in creating FAIR data in the medical sector using digital concept mapping, a systematic mixed methods approach. Thirty-eight principal investigators (PIs) were recruited from North America, Europe, and Oceania. Our analysis revealed five clusters rated according to perceived relevance: ‘Efficiency and collaboration’ (rating 7.23), ‘Privacy and security’ (rating 7.18), ‘Data management standards’ (rating 7.16), ‘Organization of services’ (rating 6.98), and ‘Ownership’ (rating 6.28). All five clusters scored relatively high and within a narrow range (i.e., 6.28–7.69), implying that each cluster likely influences researchers’ decision-making processes. PIs harbor a positive view of FAIR data sharing, as exemplified by participants highly prioritizing ‘Efficiency and collaboration’. However, the other four clusters received only modestly lower ratings and largely contained barriers to FAIR data sharing. When viewed collectively, the benefits of efficiency and collaboration may not be sufficient in propelling FAIR data sharing. Arguably, until more of these reported barriers are addressed, widespread support of FAIR data will not translate into widespread practice. This research lays the preliminary foundation for conducting targeted large-scale research into FAIR data practices in the medical research community.
{"title":"FAIR data sharing: An international perspective on why medical researchers are lagging behind","authors":"L. Rainey, J. Lutomski, M. Broeders","doi":"10.1177/20539517231171052","DOIUrl":"https://doi.org/10.1177/20539517231171052","url":null,"abstract":"FAIR data, that is, Findable, Accessible, Interoperable, and Reusable data, and Big Data intersect across issues related to data storage, access, and processing. The solution-oriented FAIR principles serve an integral role in improving Big Data; yet to date, the implementation of FAIR in multiple sectors has been fragmented. We conducted an exploratory analysis to identify incentives and barriers in creating FAIR data in the medical sector using digital concept mapping, a systematic mixed methods approach. Thirty-eight principal investigators (PIs) were recruited from North America, Europe, and Oceania. Our analysis revealed five clusters rated according to perceived relevance: ‘Efficiency and collaboration’ (rating 7.23), ‘Privacy and security’ (rating 7.18), ‘Data management standards’ (rating 7.16), ‘Organization of services’ (rating 6.98), and ‘Ownership’ (rating 6.28). All five clusters scored relatively high and within a narrow range (i.e., 6.28–7.69), implying that each cluster likely influences researchers’ decision-making processes. PIs harbor a positive view of FAIR data sharing, as exemplified by participants highly prioritizing ‘Efficiency and collaboration’. However, the other four clusters received only modestly lower ratings and largely contained barriers to FAIR data sharing. When viewed collectively, the benefits of efficiency and collaboration may not be sufficient in propelling FAIR data sharing. Arguably, until more of these reported barriers are addressed, widespread support of FAIR data will not translate into widespread practice. This research lays the preliminary foundation for conducting targeted large-scale research into FAIR data practices in the medical research community.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45542784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01Epub Date: 2023-02-20DOI: 10.1177/20539517231155060
Jeffrey W Lockhart
Research on scientific/intellectual movements, and social movements generally, tends to focus on resources and conditions outside the substance of the movements, such as funding and publication opportunities or the prestige and networks of movement actors. Drawing on Pinch's theory of technologies as institutions, I argue that research methods can also serve as resources for scientific movements by institutionalizing their ideas in research practice. I demonstrate the argument with the case of neuroscience, where the adoption of machine learning changed how scientists think about measurement and modeling of group difference. This provided an opportunity for members of the sex difference movement by offering a 'truly categorical' quantitative methodology that aligned more closely with their understanding of male and female brains and bodies as categorically distinct. The result was a flurry of publications and symbiotic relationships with other researchers that rescued a scientific movement which had been growing increasingly untenable under the prior methodological regime of univariate, frequentist analyses. I call for increased sociological attention to the inner workings of technologies that we typically black box in light of their potential consequences for the social world. I also suggest that machine learning in particular might have wide-reaching implications for how we conceive of human groups beyond sex, including race, sexuality, criminality, and political position, where scientists are just beginning to adopt its methods.
{"title":"Because the machine can discriminate: How machine learning serves and transforms biological explanations of human difference.","authors":"Jeffrey W Lockhart","doi":"10.1177/20539517231155060","DOIUrl":"10.1177/20539517231155060","url":null,"abstract":"<p><p>Research on scientific/intellectual movements, and social movements generally, tends to focus on resources and conditions outside the substance of the movements, such as funding and publication opportunities or the prestige and networks of movement actors. Drawing on Pinch's theory of technologies as institutions, I argue that research methods can also serve as resources for scientific movements by institutionalizing their ideas in research practice. I demonstrate the argument with the case of neuroscience, where the adoption of machine learning changed how scientists think about measurement and modeling of group difference. This provided an opportunity for members of the sex difference movement by offering a 'truly categorical' quantitative methodology that aligned more closely with their understanding of male and female brains and bodies as categorically distinct. The result was a flurry of publications and symbiotic relationships with other researchers that rescued a scientific movement which had been growing increasingly untenable under the prior methodological regime of univariate, frequentist analyses. I call for increased sociological attention to the inner workings of technologies that we typically black box in light of their potential consequences for the social world. I also suggest that machine learning in particular might have wide-reaching implications for how we conceive of human groups beyond sex, including race, sexuality, criminality, and political position, where scientists are just beginning to adopt its methods.</p>","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10704893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43128359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/20539517231177617
Bartosz Ślosarski
The article proposes the theoretical category of data arenas as a relational field for strategic actors in diverse areas of the contentious politics of data (Beraldo and Milan, 2019). The paper argues that the conceptualization of data activism needs to be related to the immediate data arena in which the action takes place, in order to select the interactive opportunities and threats for emerging data-driven repertoires of action. To fully work through the relational dynamics of data activism, it is necessary to move from a conceptualization of data infrastructure to the notion of data arenas as an ‘open-ended bundle of rules and resources that allows certain kinds of interaction to proceed’ (Jasper, 2006: 141). Using the case of environmental data activism, I highlight four key dimensions to study: (a) strategic use of data as capital that differentiates and positions actors, as well as influences their further choices; (b) practices of defining the boundaries of the problem on which the arena focuses and outlining the pool of actors who participate in the process of solving it; (3) sets of relationships among the outlined pool of actors which represent opportunities and threats for the actors, related to the position they occupy within an arena; and (4) power as the ability to control and shape an arena. Data arena approach shed new light on data activism as a relational practice, combining the latest developments in research on data contexts and the political situatedness of data with the emerging field of research on data activism.
{"title":"Data arenas: The relational dynamics of data activism","authors":"Bartosz Ślosarski","doi":"10.1177/20539517231177617","DOIUrl":"https://doi.org/10.1177/20539517231177617","url":null,"abstract":"The article proposes the theoretical category of data arenas as a relational field for strategic actors in diverse areas of the contentious politics of data (Beraldo and Milan, 2019). The paper argues that the conceptualization of data activism needs to be related to the immediate data arena in which the action takes place, in order to select the interactive opportunities and threats for emerging data-driven repertoires of action. To fully work through the relational dynamics of data activism, it is necessary to move from a conceptualization of data infrastructure to the notion of data arenas as an ‘open-ended bundle of rules and resources that allows certain kinds of interaction to proceed’ (Jasper, 2006: 141). Using the case of environmental data activism, I highlight four key dimensions to study: (a) strategic use of data as capital that differentiates and positions actors, as well as influences their further choices; (b) practices of defining the boundaries of the problem on which the arena focuses and outlining the pool of actors who participate in the process of solving it; (3) sets of relationships among the outlined pool of actors which represent opportunities and threats for the actors, related to the position they occupy within an arena; and (4) power as the ability to control and shape an arena. Data arena approach shed new light on data activism as a relational practice, combining the latest developments in research on data contexts and the political situatedness of data with the emerging field of research on data activism.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41726502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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