Monday, 20 June 2022

Seeking Co-Editors for Big Data & Society

Come join us!

The journal Big Data & Society (https://journals.sagepub.com/home/bds) seeks expressions of interest in serving as a Co-Editor for a three year term beginning sometime between September to December 2022. 

As a Co-Editor for Big Data & Society, you will help shape scholarly work around Big Data practices and society, and interact with some of the most cutting-edge research in the field. As part of the review process Co-Editors are assigned papers and oversee identifying and inviting referees, synthesizing a response to the author from referee's reviews, and recommending a decision to the Managing Editor. 

If you are interested in being considered please fill out this simple form by August 8, 2022. If you have questions, please contact the Managing Editor of the journal, Dr. Matthew Zook at zook@uky.edu 

Information about the journal’s current editorial team is available here



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Big Data & Society (BD&S) is an Open Access peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing, and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business and governmental relations, expertise, methods, concepts and knowledge. 

Big Data & Society has an Impact Factor of 5.987 according to the Journal Citation Reports by Web of Science Group, 2021. The 5-Year Impact Factor of the journal is 8.118. The journal is now ranked as the highest journal out of 110 in the Social Sciences Interdisciplinary domain of the multidisciplinary Social Sciences Citation Index (SSCI). 

Thursday, 16 June 2022

‘Real-time’ air quality channels: A technology review of emerging environmental alert systems by Kayla Schulte

Kayla Schulte introduces a new paper on, "‘Real-time’ air quality channels: A technology review of emerging environmental alert systems", out in Big Data & Society  doi:10.1177/20539517221101346. First published June 15, 2022.

Video abstract


Abstract.

Poor air quality is a pressing global challenge contributing to adverse health impacts around the world. In the past decade, there has been a rapid proliferation of air quality information delivered via sensors, apps, websites or other media channels in near real-time and at increasingly localized geographic scales. This paper explores the growing emphasis on self-monitoring and digital platforms to supply informational interventions for reducing pollution exposures and improving health outcomes at the individual level. It presents a technological case study that characterizes emerging air quality information communication mechanisms, or ‘AQ channels’, while drawing upon examples throughout the literature. The questions are posed: which air quality channels are ‘freely’ available to individuals in London, UK, and when and where are they accessed? Digital trace data and metadata associated with 54 air quality channels are synthesized narratively and graphically. Results reveal air quality channels derive air pollution estimates using common data sources, display disparate messaging, adopt variable geographic scales for reporting ‘readings’ and maintain psychosocial barriers to access and adoption of exposure-reducing behaviours. The results also point to a clear association between the publication of a high-profile news article about air pollution and increased air quality channel access. These findings illuminate a need for greater transparency around how air quality channels generate personalized air pollution exposure estimates and tailor messaging. The paper concludes by calling for air quality channel developers to exercise co-creative methods that can support sustainable, democratic data and knowledge production around air quality, while critically approaching disproportionate patterns of both pollution and information exposure.

Keywords: Air pollutionenvironmental datadigital tracesknowledge productionplatformsdigital inequality



Tuesday, 14 June 2022

Journal will be on break from July 10th to August 14, 2022

The editorial team of the journal Big Data & Society will have a break from July 10th to August 14th.  

Please accept any delays in processing and reviewing your submission, and in related correspondence during that time. Thank you!

Wednesday, 25 May 2022

Call for Special Theme Proposals for Big Data & Society (due August 15, 2022)

 Call for Special Theme Proposals for Big Data & Society

 

The SAGE open access journal Big Data & Society (BD&S) is soliciting proposals for a Special Theme to be published in 2023/24. BD&S is the highest ranked journal in the Social Sciences Interdisciplinary category of the Social Sciences Citation Index (SSCI) with an impact factor of 5.987 in 2021. BD&S is a peer-reviewed, interdisciplinary, scholarly journal that publishes research about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business and government relations, expertise, methods, concepts and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practices that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government and crowd-sourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes.


Special Themes can consist of a combination of Original Research Articles (10,000 words; maximum 6), Commentaries (3,000 words; maximum 4) and one Editorial (3,000 words). All Special Theme content will be waived Article Processing Charges. All submissions will go through the Journal’s standard peer review process.

 

Past special themes for the journal have included: Knowledge Production; Algorithms in Culture; Data Associations in Global Law and Policy; The Cloud, the Crowd, and the City; Veillance and Transparency; Practicing, Materializing and Contesting Environmental Data; Spatial Big Data; Critical Data Studies; Social Media & Society; Assumptions of Sociality; Data & Agency; Health Data Ecosystems; Algorithmic Normativities; Big Data and Surveillance; The Turn to AI in Governing Communication Online; The Personalization of Insurance; Heritage in a World of Big DataStudying the COVID-19 Infodemic at Scale; Digital Phenotyping; Machine Anthropology; and Data, Power, and Racial Formation. See http://journals.sagepub.com/page/bds/collections/index for full listing

 

While open to submissions on any theme related to Big Data we particularly welcome proposals related to racialisation, indigenous data, health and education.


Format of Special Theme Proposals

Researchers interested in proposing a Special Theme should submit an outline with the following information.

 

  • An overview of the proposed theme, how it relates to existing research and the aims and scope of the Journal, and the ways it seeks to expand critical scholarly research on Big Data.

  • A list of titles, abstracts, authors and brief biographies. For each, the type of submission (ORA, Commentary) should also be indicated. If the proposal is the result of a workshop or conference that should also be indicated.
    Short Bios of the Guest Editors including affiliations and previous work in the field of Big Data studies. Links to homepages, Google Scholar profiles or CVs are welcome, although we don’t require CV submissions.

  • A proposed timing for submission to Manuscript Central. This should be in line with the timeline outlined below.

 

Information on the types of submissions published by the Journal and other guidelines is available at https://journals.sagepub.com/author-instructions/BDS  .

 

Timeline for Proposals

Please submit proposals by August 15, 2022 to the Managing Editor of the Journal, Prof. Matthew Zook at zook@uky.edu. The Editorial Team of BD&S will review proposals and make a decision by October 2022. Manuscripts would be submitted to the journal (via manuscript central) by or before January/February 2023. For further information or discuss potential themes please contact Matthew Zook at zook@uky.edu.

 


Wednesday, 27 April 2022

Hey Siri, can you hear me now? A framework for building natural language processing tools that advance linguistic justice

Nee J, Smith GM, Sheares A, Rustagi I. Linguistic justice as a framework for designing, developing, and managing natural language processing tools. Big Data & Society. January 2022. doi:10.1177/20539517221090930

The increasing ubiquity of natural language processing (NLP) tools that learn from and use human language is undeniable. NLP-powered tools now produce records of court hearings, inform job interview analyses, respond to our verbal requests through smartphones, and more. However, NLP tools don’t serve all people equally: they often perform better for certain speakers and advance linguistic profiling. So, a critical question remains: how can these technologies equitably serve all members of society, regardless of their language background?

The concept of linguistic justice can be used to frame NLP tool development in a way that
centres the needs of all users, rather than prioritising speakers of privileged languages like “Standard” English. Linguistic justice is achieved when all individuals are granted equitable access to social, political, and economic life, regardless of their linguistic repertoire. Linguistic justice, then, requires that NLP tools serve diverse speakers and signers equitably.

Our commentary examines in detail two issues with current NLP tool development. First, if NLP tools learn from datasets that lack sufficient data from speakers of minoritised language varieties, those tools may underperform for those users. Secondly, NLP systems can use language to infer information about the identities of users - a process known as linguistic profiling. Even when protected information (e.g., race, gender) is not directly provided to an NLP system, the system may still infer a users’ identity from features of their language use. Inferred characteristics may then be used to mediate access to goods, services, and opportunities, resulting in unlawful discrimination.

We present nine specific actions that researchers, developers, and business leaders can take to design, develop, and manage NLP systems that advance linguistic justice. This includes, for example, working with diverse language communities in participatory and empowering ways, ensuring language data is labeled by people familiar with the particular language variety, and examining and altering power structures so that the needs and perspectives of those at the margins are prioritised.

Instead of being comfortable with the status quo, this work requires imagining and working towards a world where users of all language varieties are able to equitably access social, economic, and political life. It requires rethinking how we collect data and what data we value in NLP development. Our nine actions provide a path forward toward that world – whereby NLP systems can advance linguistic justice and thereby, social justice.

Tuesday, 19 April 2022

In Search of the Citizen

Heather Broomfield & Lisa Reutter

Broomfield H, Reutter L. In search of the citizen in the datafication of public administration. Big Data & Society. January 2022. doi:10.1177/20539517221089302
  
How are citizen perspectives problematised and included in policy and practitioner discourse in the datafication of public administration?

During an initial analysis of empirical material collected to map the Norwegian public sector, we were struck by the discursive absence of citizens in the realisation of this all-encompassing administrative reform. This sparked our curiosity, leading us to raise our gaze from the organisational to the system level and investigate who was invited to participate in policy formation and how citizens were described, in both the resulting documents and  practitioner discourse. 

The Norwegian data-driven context is particularly interesting to investigate. The state has collected vast amounts of data on the population for decades, the recirculation of which can make data-driven public administration realisable on a scale unimaginable in many other countries. Norway is also characterised by a corporative pluralism where collaboration with externals and inter-dependent decision making with interest organisations and business representative organisations, is deemed fundamental to policy making (Rokkan, 1966). 

Unexpectedly, we identified a paternalistic and top-down technocratic approach to citizen engagement with non-participation particularly apparent at the policy making level. Citizens and civil society are reduced to a passive but demanding ‘user’ to be served by the public sector. This is in direct contrast to active engagement with the private sector during all phases - from policy production through to implementation. 

Datafication often escapes democratic decision-making as the context, values, and agendas of this administrative reform are obscured from citizens and civil society. We hope this paper sparks interest among practitioners and scholars alike. 

Sunday, 13 March 2022

Special Theme on Data, Power, and Racial Formation

Data, Power, and Racial Formation: Situating Data within Interlocking Systems of Oppression


The promise of big data lies in its ability to draw connections and reveal patterns about social life. There are growing concerns, however, that the reliance on big data can threaten not only to automate discrimination and oppression but also to become central mechanisms through which racism operates. 

Critical observers encourage closer attention be paid to how power manifests in and through the application of big data as well as through automated systems and so-called ‘smart’ technologies. This special issue heeds their advice by exploring how race and racism are entangled in the collection and use of data. The papers in this collection showcase a range of interdisciplinary insights to demonstrate how data studies might benefit from deeper engagement with intellectual schools of thought concerned with race and racism—both theoretically and practically. They illustrate how theories of race and racism can enhance understandings of big data’s material impacts and can inform approaches to addressing these impacts. Authors make productive inroads regarding how data emerge in and through racial projects as they intersect with systems of class, colonialism, disability, gender, and sexuality. 

Looking at how big data reflects entanglements of racialised power prompts a range of critical questions. How do modes of datafication normalise racial classification systems and mask their sociocultural underpinnings? To what extent can big data work in the service of liberatory agendas? What are the opportunities and risks of practices and systems that promise more equitable outcomes? 

In answering these questions, this collection captures connections and tensions between data and racial formation. Racial formation, often associated with work by sociologists Michael Omi and Howard Winant, captures the relationships between socio-economic and political changes and the shifting nature and value of racial categories, including the meanings they come to reflect. 

The collection thus builds upon longstanding concerns about data and racialised power. Its aim is to bring them into dialogue with more recent discussions in critical data studies. Such work includes, but is not limited to, how data animate knowledge systems in ways that may be actively harm Black and Indigenous peoples, as well as other minoritised communities, and how race and racialised value systems appear in datasets, machine learning models, and ‘smart’ home devices. In addition to contributing to scholarly debates about how data are mobilised to generate racial formations, authors’ insights support strategies for anti-racist movements by drawing attention to how they can be challenged and disrupted.

Papers in this special issue address how data become implicated within the interlocking systems of domination and oppression, doing so in ways that are attentive to effects that emerge in everyday lives and livelihoods. Phan and Wark reconsider how datafied processes evince new shifts in processes of racialisation. Hatch examines how the governance of COVID-19-related health data became a site where data about racial death became framed as a matter for liberal reform but served to support racist social systems. Henne, Shelby, and Harb demonstrate how racial capitalism can advance understandings of data capital and the inequalities it can generate, doing so through an in-depth study of digital platforms used for intervening in gender-based violence. Sooriyakumaran is similarly concerned about racialised inequalities etched and shaped by capitalist relations, considering how they manifest in digitised residential tenancy databases in Australia. Crooks (2021) shows how non-profit efforts to make public schools more “data driven” can be understood as racial projects. Anantharajah (2021) attends to how racial formation takes shape through colonial data projects, drawing on ethnographic research on climate finance governance conducted in Fiji. 

This special thematic issue offers one set of responses to the pressing need to critically examine how race and racism are entangled in the collection and use of data. It brings together longstanding and emergent concerns regarding data’s role within racial formation. It also reflects on recent cultural and political developments as well as geopolitical and socio-technical shifts. In doing so, this collection of papers marks an attempt to illuminate how data become implicated within the interlocking systems of domination and oppression that affect everyday lives and livelihoods. We recognise that many others are working on similarly projects. We therefore hope the collection serves as a productive resource for readers from a range of fields and contributes to a generative dialogue that crosses disciplinary boundaries.