Publisher: Sage Publications   (Total: 1089 journals)

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Showing 1 - 200 of 1089 Journals sorted alphabetically
AADE in Practice     Hybrid Journal   (Followers: 6)
Abstracts in Anthropology     Full-text available via subscription   (Followers: 24)
Academic Pathology     Open Access   (Followers: 5)
Accounting History     Hybrid Journal   (Followers: 17, SJR: 0.527, CiteScore: 1)
Acta Radiologica     Hybrid Journal   (Followers: 1, SJR: 0.754, CiteScore: 2)
Acta Radiologica Open     Open Access   (Followers: 2)
Acta Sociologica     Hybrid Journal   (Followers: 39, SJR: 0.939, CiteScore: 2)
Action Research     Hybrid Journal   (Followers: 51, SJR: 0.308, CiteScore: 1)
Active Learning in Higher Education     Hybrid Journal   (Followers: 360, SJR: 1.397, CiteScore: 2)
Adaptive Behavior     Hybrid Journal   (Followers: 9, SJR: 0.288, CiteScore: 1)
Administration & Society     Hybrid Journal   (Followers: 14, SJR: 0.675, CiteScore: 1)
Adoption & Fostering     Hybrid Journal   (Followers: 24, SJR: 0.313, CiteScore: 0)
Adsorption Science & Technology     Open Access   (Followers: 8, SJR: 0.258, CiteScore: 1)
Adult Education Quarterly     Hybrid Journal   (Followers: 237, SJR: 0.566, CiteScore: 2)
Adult Learning     Hybrid Journal   (Followers: 44)
Advances in Dental Research     Hybrid Journal   (Followers: 9, SJR: 1.791, CiteScore: 4)
Advances in Developing Human Resources     Hybrid Journal   (Followers: 31, SJR: 0.614, CiteScore: 2)
Advances in Mechanical Engineering     Open Access   (Followers: 136, SJR: 0.272, CiteScore: 1)
Advances in Methods and Practices in Psychological Science     Full-text available via subscription   (Followers: 11)
Advances in Structural Engineering     Full-text available via subscription   (Followers: 46, SJR: 0.599, CiteScore: 1)
Advances in Tumor Virology     Open Access   (Followers: 3, SJR: 0.108, CiteScore: 0)
AERA Open     Open Access   (Followers: 10)
Affilia     Hybrid Journal   (Followers: 5, SJR: 0.496, CiteScore: 1)
Agrarian South : J. of Political Economy     Hybrid Journal   (Followers: 2)
Air, Soil & Water Research     Open Access   (Followers: 13, SJR: 0.214, CiteScore: 1)
Alexandria : The J. of National and Intl. Library and Information Issues     Full-text available via subscription   (Followers: 67)
Allergy & Rhinology     Open Access   (Followers: 4)
AlterNative : An Intl. J. of Indigenous Peoples     Full-text available via subscription   (Followers: 12, SJR: 0.194, CiteScore: 0)
Alternative Law J.     Hybrid Journal   (Followers: 11, SJR: 0.176, CiteScore: 0)
Alternatives : Global, Local, Political     Hybrid Journal   (Followers: 12, SJR: 0.351, CiteScore: 1)
American Behavioral Scientist     Hybrid Journal   (Followers: 24, SJR: 0.982, CiteScore: 2)
American Economist     Hybrid Journal   (Followers: 8)
American Educational Research J.     Hybrid Journal   (Followers: 227, SJR: 2.913, CiteScore: 3)
American J. of Alzheimer's Disease and Other Dementias     Hybrid Journal   (Followers: 19, SJR: 0.67, CiteScore: 2)
American J. of Cosmetic Surgery     Hybrid Journal   (Followers: 8)
American J. of Evaluation     Hybrid Journal   (Followers: 17, SJR: 0.646, CiteScore: 2)
American J. of Health Promotion     Hybrid Journal   (Followers: 34, SJR: 0.807, CiteScore: 1)
American J. of Hospice and Palliative Medicine     Hybrid Journal   (Followers: 43, SJR: 0.65, CiteScore: 1)
American J. of Law & Medicine     Full-text available via subscription   (Followers: 12, SJR: 0.204, CiteScore: 1)
American J. of Lifestyle Medicine     Hybrid Journal   (Followers: 6, SJR: 0.431, CiteScore: 1)
American J. of Medical Quality     Hybrid Journal   (Followers: 12, SJR: 0.777, CiteScore: 1)
American J. of Men's Health     Open Access   (Followers: 9, SJR: 0.595, CiteScore: 2)
American J. of Rhinology and Allergy     Hybrid Journal   (Followers: 9, SJR: 0.972, CiteScore: 2)
American J. of Sports Medicine     Hybrid Journal   (Followers: 215, SJR: 3.949, CiteScore: 6)
American Politics Research     Hybrid Journal   (Followers: 33, SJR: 1.313, CiteScore: 1)
American Review of Public Administration     Hybrid Journal   (Followers: 21, SJR: 2.062, CiteScore: 2)
American Sociological Review     Hybrid Journal   (Followers: 330, SJR: 6.333, CiteScore: 6)
American String Teacher     Full-text available via subscription   (Followers: 2)
Analytical Chemistry Insights     Open Access   (Followers: 26, SJR: 0.224, CiteScore: 1)
Angiology     Hybrid Journal   (Followers: 3, SJR: 0.849, CiteScore: 2)
Animation     Hybrid Journal   (Followers: 14, SJR: 0.197, CiteScore: 0)
Annals of Clinical Biochemistry     Hybrid Journal   (Followers: 10, SJR: 0.634, CiteScore: 1)
Annals of Otology, Rhinology & Laryngology     Hybrid Journal   (Followers: 17, SJR: 0.807, CiteScore: 1)
Annals of Pharmacotherapy     Hybrid Journal   (Followers: 54, SJR: 1.096, CiteScore: 2)
Annals of the American Academy of Political and Social Science     Hybrid Journal   (Followers: 48, SJR: 1.225, CiteScore: 3)
Annals of the ICRP     Hybrid Journal   (Followers: 4, SJR: 0.548, CiteScore: 1)
Anthropocene Review     Hybrid Journal   (Followers: 8, SJR: 3.341, CiteScore: 7)
Anthropological Theory     Hybrid Journal   (Followers: 42, SJR: 0.739, CiteScore: 1)
Antitrust Bulletin     Hybrid Journal   (Followers: 11)
Antiviral Chemistry and Chemotherapy     Open Access   (Followers: 2, SJR: 0.635, CiteScore: 2)
Antyajaa : Indian J. of Women and Social Change     Hybrid Journal  
Applied Biosafety     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Applied Psychological Measurement     Hybrid Journal   (Followers: 23, SJR: 1.17, CiteScore: 1)
Applied Spectroscopy     Full-text available via subscription   (Followers: 26, SJR: 0.489, CiteScore: 2)
Armed Forces & Society     Hybrid Journal   (Followers: 22, SJR: 0.29, CiteScore: 1)
Arts and Humanities in Higher Education     Hybrid Journal   (Followers: 42, SJR: 0.305, CiteScore: 1)
Asia Pacific Media Educator     Hybrid Journal   (Followers: 1, SJR: 0.23, CiteScore: 0)
Asia-Pacific J. of Management Research and Innovation     Full-text available via subscription   (Followers: 3)
Asia-Pacific J. of Public Health     Hybrid Journal   (Followers: 11, SJR: 0.558, CiteScore: 1)
Asian and Pacific Migration J.     Full-text available via subscription   (Followers: 9, SJR: 0.324, CiteScore: 1)
Asian Cardiovascular and Thoracic Annals     Hybrid Journal   (Followers: 2, SJR: 0.305, CiteScore: 0)
Asian J. of Comparative Politics     Hybrid Journal   (Followers: 5)
Asian J. of Legal Education     Full-text available via subscription   (Followers: 4)
Asian J. of Management Cases     Hybrid Journal   (Followers: 6, SJR: 0.101, CiteScore: 0)
ASN Neuro     Open Access   (Followers: 2, SJR: 1.534, CiteScore: 3)
Assessment     Hybrid Journal   (Followers: 17, SJR: 1.519, CiteScore: 3)
Assessment for Effective Intervention     Hybrid Journal   (Followers: 16, SJR: 0.578, CiteScore: 1)
Australasian Psychiatry     Hybrid Journal   (Followers: 18, SJR: 0.433, CiteScore: 1)
Australian & New Zealand J. of Psychiatry     Hybrid Journal   (Followers: 29, SJR: 1.801, CiteScore: 2)
Australian and New Zealand J. of Criminology     Hybrid Journal   (Followers: 531, SJR: 0.612, CiteScore: 1)
Australian J. of Career Development     Hybrid Journal   (Followers: 4)
Australian J. of Education     Hybrid Journal   (Followers: 42, SJR: 0.403, CiteScore: 1)
Australian J. of Management     Hybrid Journal   (Followers: 13, SJR: 0.497, CiteScore: 1)
Autism     Hybrid Journal   (Followers: 338, SJR: 1.739, CiteScore: 4)
Autism & Developmental Language Impairments     Open Access   (Followers: 12)
Behavior Modification     Hybrid Journal   (Followers: 12, SJR: 0.877, CiteScore: 2)
Behavioral and Cognitive Neuroscience Reviews     Hybrid Journal   (Followers: 26)
Bible Translator     Hybrid Journal   (Followers: 13)
Biblical Theology Bulletin     Hybrid Journal   (Followers: 19, SJR: 0.184, CiteScore: 0)
Big Data & Society     Open Access   (Followers: 52)
Biochemistry Insights     Open Access   (Followers: 7)
Bioinformatics and Biology Insights     Open Access   (Followers: 12, SJR: 1.141, CiteScore: 2)
Biological Research for Nursing     Hybrid Journal   (Followers: 7, SJR: 0.685, CiteScore: 2)
Biomarker Insights     Open Access   (Followers: 1, SJR: 0.81, CiteScore: 2)
Biomarkers in Cancer     Open Access   (Followers: 11)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Informatics Insights     Open Access   (Followers: 9)
Bioscope: South Asian Screen Studies     Hybrid Journal   (Followers: 3, SJR: 0.235, CiteScore: 0)
BMS: Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique     Hybrid Journal   (Followers: 4, SJR: 0.226, CiteScore: 0)
Body & Society     Hybrid Journal   (Followers: 27, SJR: 1.531, CiteScore: 3)
Bone and Tissue Regeneration Insights     Open Access   (Followers: 2)
Brain and Neuroscience Advances     Open Access  
Breast Cancer : Basic and Clinical Research     Open Access   (Followers: 11, SJR: 0.823, CiteScore: 2)
British J. of Music Therapy     Hybrid Journal   (Followers: 8)
British J. of Occupational Therapy     Hybrid Journal   (Followers: 217, SJR: 0.323, CiteScore: 1)
British J. of Pain     Hybrid Journal   (Followers: 27, SJR: 0.579, CiteScore: 2)
British J. of Politics and Intl. Relations     Hybrid Journal   (Followers: 33, SJR: 0.91, CiteScore: 2)
British J. of Visual Impairment     Hybrid Journal   (Followers: 13, SJR: 0.337, CiteScore: 1)
British J.ism Review     Hybrid Journal   (Followers: 18)
BRQ Business Review Quarterly     Open Access   (Followers: 1)
Building Acoustics     Hybrid Journal   (Followers: 4, SJR: 0.215, CiteScore: 1)
Building Services Engineering Research & Technology     Hybrid Journal   (Followers: 3, SJR: 0.583, CiteScore: 1)
Bulletin of Science, Technology & Society     Hybrid Journal   (Followers: 8)
Business & Society     Hybrid Journal   (Followers: 13)
Business and Professional Communication Quarterly     Hybrid Journal   (Followers: 8, SJR: 0.348, CiteScore: 1)
Business Information Review     Hybrid Journal   (Followers: 17, SJR: 0.279, CiteScore: 0)
Business Perspectives and Research     Hybrid Journal   (Followers: 3)
Cahiers Élisabéthains     Hybrid Journal   (Followers: 1, SJR: 0.111, CiteScore: 0)
Calcutta Statistical Association Bulletin     Full-text available via subscription   (Followers: 1)
California Management Review     Hybrid Journal   (Followers: 32, SJR: 2.209, CiteScore: 4)
Canadian J. of Kidney Health and Disease     Open Access   (Followers: 6, SJR: 1.007, CiteScore: 2)
Canadian J. of Nursing Research (CJNR)     Hybrid Journal   (Followers: 13)
Canadian J. of Occupational Therapy     Hybrid Journal   (Followers: 142, SJR: 0.626, CiteScore: 1)
Canadian J. of Psychiatry     Hybrid Journal   (Followers: 27, SJR: 1.769, CiteScore: 3)
Canadian J. of School Psychology     Hybrid Journal   (Followers: 12, SJR: 0.266, CiteScore: 1)
Canadian Pharmacists J. / Revue des Pharmaciens du Canada     Hybrid Journal   (Followers: 3, SJR: 0.536, CiteScore: 1)
Cancer Control     Open Access   (Followers: 1)
Cancer Growth and Metastasis     Open Access   (Followers: 1)
Cancer Informatics     Open Access   (Followers: 4, SJR: 0.64, CiteScore: 1)
Capital and Class     Hybrid Journal   (Followers: 8, SJR: 0.282, CiteScore: 1)
Cardiac Cath Lab Director     Full-text available via subscription  
Cardiovascular and Thoracic Open     Open Access  
Career Development and Transition for Exceptional Individuals     Hybrid Journal   (Followers: 9, SJR: 0.44, CiteScore: 1)
Cartilage     Hybrid Journal   (Followers: 5, SJR: 0.889, CiteScore: 3)
Cell and Tissue Transplantation and Therapy     Open Access   (Followers: 2)
Cell Transplantation     Open Access   (Followers: 4, SJR: 1.023, CiteScore: 3)
Cephalalgia     Hybrid Journal   (Followers: 8, SJR: 1.581, CiteScore: 3)
Cephalalgia Reports     Open Access   (Followers: 2)
Child Language Teaching and Therapy     Hybrid Journal   (Followers: 35, SJR: 0.501, CiteScore: 1)
Child Maltreatment     Hybrid Journal   (Followers: 9, SJR: 1.22, CiteScore: 3)
Child Neurology Open     Open Access   (Followers: 5)
Childhood     Hybrid Journal   (Followers: 19, SJR: 0.894, CiteScore: 2)
Childhood Obesity and Nutrition     Open Access   (Followers: 11)
China Information     Hybrid Journal   (Followers: 7, SJR: 0.767, CiteScore: 2)
China Report     Hybrid Journal   (Followers: 10, SJR: 0.221, CiteScore: 0)
Chinese J. of Sociology     Full-text available via subscription   (Followers: 4)
Chronic Illness     Hybrid Journal   (Followers: 6, SJR: 0.672, CiteScore: 2)
Chronic Respiratory Disease     Hybrid Journal   (Followers: 9, SJR: 0.808, CiteScore: 2)
Chronic Stress     Open Access  
Citizenship, Social and Economics Education     Full-text available via subscription   (Followers: 6, SJR: 0.145, CiteScore: 0)
Cleft Palate-Craniofacial J.     Hybrid Journal   (Followers: 8, SJR: 0.757, CiteScore: 1)
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical and Applied Thrombosis/Hemostasis     Open Access   (Followers: 32, SJR: 0.49, CiteScore: 1)
Clinical and Translational Neuroscience     Open Access  
Clinical Case Studies     Hybrid Journal   (Followers: 3, SJR: 0.364, CiteScore: 1)
Clinical Child Psychology and Psychiatry     Hybrid Journal   (Followers: 45, SJR: 0.73, CiteScore: 2)
Clinical EEG and Neuroscience     Hybrid Journal   (Followers: 6, SJR: 0.552, CiteScore: 2)
Clinical Ethics     Hybrid Journal   (Followers: 10, SJR: 0.296, CiteScore: 1)
Clinical Medicine Insights : Arthritis and Musculoskeletal Disorders     Open Access   (Followers: 3, SJR: 0.537, CiteScore: 2)
Clinical Medicine Insights : Blood Disorders     Open Access   (SJR: 0.314, CiteScore: 2)
Clinical Medicine Insights : Cardiology     Open Access   (Followers: 6, SJR: 0.686, CiteScore: 2)
Clinical Medicine Insights : Case Reports     Open Access   (Followers: 1, SJR: 0.283, CiteScore: 1)
Clinical Medicine Insights : Circulatory, Respiratory and Pulmonary Medicine     Open Access   (Followers: 3, SJR: 0.425, CiteScore: 2)
Clinical Medicine Insights : Ear, Nose and Throat     Open Access   (Followers: 1)
Clinical Medicine Insights : Endocrinology and Diabetes     Open Access   (Followers: 33, SJR: 0.63, CiteScore: 2)
Clinical Medicine Insights : Oncology     Open Access   (Followers: 3, SJR: 1.129, CiteScore: 3)
Clinical Medicine Insights : Pediatrics     Open Access   (Followers: 3)
Clinical Medicine Insights : Psychiatry     Open Access   (Followers: 10)
Clinical Medicine Insights : Reproductive Health     Open Access   (Followers: 2, SJR: 0.776, CiteScore: 0)
Clinical Medicine Insights : Therapeutics     Open Access   (Followers: 1, SJR: 0.172, CiteScore: 0)
Clinical Medicine Insights : Trauma and Intensive Medicine     Open Access   (Followers: 4)
Clinical Medicine Insights : Urology     Open Access   (Followers: 3)
Clinical Medicine Insights : Women's Health     Open Access   (Followers: 4)
Clinical Nursing Research     Hybrid Journal   (Followers: 31, SJR: 0.471, CiteScore: 1)
Clinical Pathology     Open Access   (Followers: 3)
Clinical Pediatrics     Hybrid Journal   (Followers: 22, SJR: 0.487, CiteScore: 1)
Clinical Psychological Science     Hybrid Journal   (Followers: 12, SJR: 3.281, CiteScore: 5)
Clinical Rehabilitation     Hybrid Journal   (Followers: 76, SJR: 1.322, CiteScore: 3)
Clinical Risk     Hybrid Journal   (Followers: 5, SJR: 0.133, CiteScore: 0)
Clinical Trials     Hybrid Journal   (Followers: 21, SJR: 2.399, CiteScore: 2)
Clothing and Textiles Research J.     Hybrid Journal   (Followers: 25, SJR: 0.36, CiteScore: 1)
Common Law World Review     Full-text available via subscription   (Followers: 18)
Communication & Sport     Hybrid Journal   (Followers: 8, SJR: 0.385, CiteScore: 1)
Communication and the Public     Hybrid Journal   (Followers: 1)
Communication Disorders Quarterly     Hybrid Journal   (Followers: 17, SJR: 0.458, CiteScore: 1)
Communication Research     Hybrid Journal   (Followers: 21, SJR: 2.171, CiteScore: 3)
Community College Review     Hybrid Journal   (Followers: 9, SJR: 1.451, CiteScore: 1)
Comparative Political Studies     Hybrid Journal   (Followers: 257, SJR: 3.772, CiteScore: 3)
Compensation & Benefits Review     Hybrid Journal   (Followers: 8)
Competition & Change     Hybrid Journal   (Followers: 11, SJR: 0.843, CiteScore: 2)
Competition and Regulation in Network Industries     Full-text available via subscription   (Followers: 8, SJR: 0.143, CiteScore: 0)
Concurrent Engineering     Hybrid Journal   (Followers: 3, SJR: 0.642, CiteScore: 2)
Conflict Management and Peace Science     Hybrid Journal   (Followers: 40, SJR: 2.441, CiteScore: 1)
Contemporary Drug Problems     Full-text available via subscription   (Followers: 3, SJR: 0.609, CiteScore: 2)
Contemporary Education Dialogue     Hybrid Journal   (Followers: 5, SJR: 0.102, CiteScore: 0)
Contemporary Issues in Early Childhood     Full-text available via subscription   (Followers: 8, SJR: 0.766, CiteScore: 1)
Contemporary Review of the Middle East     Full-text available via subscription   (Followers: 12)
Contemporary Sociology : A J. of Reviews     Full-text available via subscription   (Followers: 35, SJR: 0.195, CiteScore: 0)
Contemporary Voice of Dalit     Full-text available via subscription   (Followers: 1)
Contexts     Hybrid Journal   (Followers: 6)

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Similar Journals
Journal Cover
Big Data & Society
Number of Followers: 52  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2053-9517
Published by Sage Publications Homepage  [1089 journals]
  • Relational data paradigms: What do we learn by taking the materiality of
           databases seriously'

    • Authors: Andrea K Thomer, Karen M Wickett
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies (e.g., relational or graph-based database management systems), but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How do the materialities of non-conventional databases differ from or align with the materialities of conventional relational systems' What properties of the database do the creators of these artifacts invoke in their rhetoric describing these systems—or in the data models underlying these digital objects' To answer these questions, we conducted a close analysis of four non-conventional scientific databases. By examining the materialities of information representation in each case, we show how scholarly communication regimes shape database materialities—and how information organization paradigms shape scholarly communication. These cases show abandonment of certain constraints of relational database construction alongside maintenance of some key relational data organization strategies. We discuss the implications that these relational data paradigms have for data use, preservation, and sharing, and discuss the need to support a plurality of data practices and paradigms.
      Citation: Big Data & Society
      PubDate: 2020-06-26T11:06:33Z
      DOI: 10.1177/2053951720934838
      Issue No: Vol. 7, No. 1 (2020)
  • Sunlight alone is not a disinfectant: Consent and the futility of opening
           Big Data black boxes (without assistance)

    • Authors: Jonathan A. Obar
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      In our attempts to achieve privacy and reputation deliverables, advocating for service providers and other data managers to open Big Data black boxes and be more transparent about consent processes, algorithmic details, and data practice is easy. Moving from this call to meaningful forms of transparency, where the Big Data details are available, useful, and manageable is more difficult. Most challenging is moving from that difficult task of meaningful transparency to the seemingly impossible scenario of achieving, consistently and ubiquitously, meaningful forms of consent, where individuals are aware of data practices and implications, understand these realities, and agree to them as well. This commentary unpacks these concerns in the online consent context. It emphasizes that self-governance fallacy pervades current approaches to achieving digital forms of privacy, exemplified by the assertion that transparency and information access alone are enough to help individuals achieve privacy and reputation protections.
      Citation: Big Data & Society
      PubDate: 2020-06-24T06:02:40Z
      DOI: 10.1177/2053951720935615
      Issue No: Vol. 7, No. 1 (2020)
  • Cyborg finance mirrors cyborg social media

    • Authors: Kamel Ajji
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      This article aims at showing the similarities between the financial and the tech sectors in their use and reliance on information and algorithms and how such dependency affects their attitude towards regulation. Drawing on Pasquale’s recommendations for reform, it sets out a proposal for a constant and independent scrutiny of internet service providers.
      Citation: Big Data & Society
      PubDate: 2020-06-23T08:40:39Z
      DOI: 10.1177/2053951720935139
      Issue No: Vol. 7, No. 1 (2020)
  • Of ‘black boxes’ and algorithmic decision-making in (higher) education
           – A commentary

    • Authors: Paul Prinsloo
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Higher education institutions have access to higher volumes and a greater variety and granularity of student data, often in real-time, than ever before. As such, the collection, analysis and use of student data are increasingly crucial in operational and strategic planning, and in delivering appropriate and effective learning experiences to students. Student data – not only in what data is (not) collected, but also how the data is framed and used – has material and discursive effects, both permanent and fleeting. We have to critically engage claims that artificial intelligence and the ever expansive/expanding systems of algorithmic decision-making provide speedy, accessible, revealing, panoramic, prophetic and smart analyses of students' risks, potential and learning needs. We need to pry open the black boxes higher education institutions (and increasingly venture capital and learning management system providers) use to admit, steer, predict and prescribe students’ learning journeys.
      Citation: Big Data & Society
      PubDate: 2020-06-22T12:07:21Z
      DOI: 10.1177/2053951720933994
      Issue No: Vol. 7, No. 1 (2020)
  • Personalization as a promise: Can Big Data change the practice of

    • Authors: Laurence Barry, Arthur Charpentier
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis with predictive analytics, and how this conceptually contradicts the collective basis of insurance. The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling. The third part attempts to assess the extent of this shift in motor insurance. Onboard devices that collect continuous driving behavioural data could import this new paradigm into these products. An examination of the current state of research on models with telematics data shows however that the epistemological leap, for now, has not happened.
      Citation: Big Data & Society
      PubDate: 2020-06-22T11:50:59Z
      DOI: 10.1177/2053951720935143
      Issue No: Vol. 7, No. 1 (2020)
  • Shareable and un-sharable knowledge

    • Authors: Mark Andrejevic
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      This article focuses on what it means to generate actionable but non-sharable information, and how this might relate to our understanding of what counts as knowledge, which typically entails some form of explanation. As automated systems sort and classify us for the purposes of dating, education, employment, health care, security, and more, we are going to want to know how and why these decisions are being made. Or, failing that, we will at least want to know, with as much clarity as possible, under what circumstances and to what uses, automated systems are being put to use. In either case, the role of narrative is inseparable from the call for transparency.
      Citation: Big Data & Society
      PubDate: 2020-06-22T11:46:59Z
      DOI: 10.1177/2053951720933917
      Issue No: Vol. 7, No. 1 (2020)
  • Public perceptions of good data management: Findings from a UK-based

    • Authors: Todd Hartman, Helen Kennedy, Robin Steedman, Rhianne Jones
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Low levels of public trust in data practices have led to growing calls for changes to data-driven systems, and in the EU, the General Data Protection Regulation provides a legal motivation for such changes. Data management is a vital component of data-driven systems, but what constitutes ‘good’ data management is not straightforward. Academic attention is turning to the question of what ‘good data’ might look like more generally, but public views are absent from these debates. This paper addresses this gap, reporting on a survey of the public on their views of data management approaches, undertaken by the authors and administered in the UK, where departure from the EU makes future data legislation uncertain. The survey found that respondents dislike the current approach in which commercial organizations control their personal data and prefer approaches that give them control over their data, that include oversight from regulatory bodies or that enable them to opt out of data gathering. Variations of data trusts – that is, structures that provide independent stewardship of data – were also preferable to the current approach, but not as widely preferred as control, oversight and opt out options. These features therefore constitute ‘good data management’ for survey respondents. These findings align only in part with principles of good data identified by policy experts and researchers. Our findings nuance understandings of good data as a concept and of good data management as a practice and point to where further research and policy action are needed.
      Citation: Big Data & Society
      PubDate: 2020-06-22T11:43:19Z
      DOI: 10.1177/2053951720935616
      Issue No: Vol. 7, No. 1 (2020)
  • Data for queer lives: How LGBTQ gender and sexuality identities challenge
           norms of demographics

    • Authors: Bonnie Ruberg, Spencer Ruelos
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      In this article, we argue that dominant norms of demographic data are insufficient for accounting for the complexities that characterize many lesbian, gay, bisexual, transgender, and queer (LGBTQ, or broadly “queer”) lives. Here, we draw from the responses of 178 people who identified as non-heterosexual or non-cisgender to demographic questions we developed regarding gender and sexual orientation. Demographic data commonly imagines identity as fixed, singular, and discrete. However, our findings suggest that, for LGBTQ people, gender and sexual identities are often multiple and in flux. An overwhelming majority of our respondents reported shifting in their understandings of their sexual identities over time. In addition, for many of our respondents, gender identity was made up of overlapping factors, including the relationship between gender and transgender identities. These findings challenge researchers to reconsider how identity is understood as and through data. Drawing from critical data studies, feminist and queer digital media studies, and social justice initiatives like Data for Black Lives, we call for a reimagining of identity-based data as “queer data” or “data for queer lives.” We offer also recommendations for researchers to develop more inclusive survey questions. At the same time, we address the ways that queer perspectives destabilize the underlying logics of data by resisting classification and “capture.” For marginalized people, the stakes of this work extend beyond academia, especially in the era of algorithms and big data when the issue of who is or is not “counted” profoundly affects visibility, access, and power in the digital realm.
      Citation: Big Data & Society
      PubDate: 2020-06-18T08:31:49Z
      DOI: 10.1177/2053951720933286
      Issue No: Vol. 7, No. 1 (2020)
  • Disambiguating the benefits and risks from public health data in the
           digital economy

    • Authors: Sarah Cheung
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      This article focuses on key roles that the ill-defined concept of ‘public benefit’ plays in accessing the public health data held by the UK’s National Health Service. Using the concept of the ‘trade-off fallacy’, this article argues that current data access and governance structures, based on particular construals of public benefit in the context of public health data, largely negate the possibility of effective control by individuals over future uses of personal health data. This generates a health data version of the trade-off fallacy that enables widespread involvement of commercial actors in personal data, despite public concerns over commercial involvement in, and potential exploitation of, public health data. The article suggests that, despite ostensibly robust regulatory and governance structures, this publicly held data is potentially subject to similar logics of accumulation as seen elsewhere in the digital economy, highlighting the inadequacies of current data regulatory frameworks in the digital era.
      Citation: Big Data & Society
      PubDate: 2020-06-18T08:21:06Z
      DOI: 10.1177/2053951720933924
      Issue No: Vol. 7, No. 1 (2020)
  • Doing nothing does something: Embodiment and data in the COVID-19 pandemic

    • Authors: Mickey Vallee
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      The COVID-19 pandemic redefines how we think about the body, physiologically and socially. But what does it mean to have and to be a body in the COVID-19 pandemic' The COVID-19 pandemic offers data scholars the unique opportunity, and perhaps obligation, to revisit and reinvent the fundamental concepts of our mediated experiences. The article critiques the data double, a longstanding concept in critical data and media studies, as incompatible with the current public health and social distancing imperative. The data double, instead, is now the presupposition of a new data entity, which will emerge out of a current data shimmer: a long-sustaining transition that blurs the older boundaries of bodies and the social, and establishes new ethical boundaries around the (in)activity and (im)mobility of doing nothing to do something. The data double faces a unique dynamic in the COVID-19 pandemic between boredom and exhaustion. Following the currently simple rule to stay home presents data scholars the opportunity to revisit the meaning of data as something given, a shimmering embodied relationship with data that contributes to the common good in a global health crisis.
      Citation: Big Data & Society
      PubDate: 2020-06-18T06:41:32Z
      DOI: 10.1177/2053951720933930
      Issue No: Vol. 7, No. 1 (2020)
  • “We called that a behavior”: The making of institutional data

    • Authors: Madisson Whitman
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Predictive uses of data are becoming widespread in institutional settings as actors seek to anticipate people and their activities. Predictive modeling is increasingly the subject of scholarly and public criticism. Less common, however, is scrutiny directed at the data that inform predictive models beyond concerns about homogenous training data or general epistemological critiques of data. In this paper, I draw from a qualitative case study set in higher education in the United States to investigate the making of data. Data analytics projects at universities have become more pervasive and intensive to better understand and anticipate undergraduate student bodies. Drawing from 12 months of ethnographic research at a large public university, I analyze the ways data personnel at the institution—data scientists, administrators, and programmers—sort student data into “attributes” and “behaviors,” where “attributes” are demographic data that students “can’t change.” “Behaviors,” in contrast, are data defined as reflective of what students can choose: attending and paying attention in class, studying on campus, among other data which personnel categorize as what students have control over. This discursive split enables the institution nudge students to make responsible choices according to behavior data that correlate with success in the predictive model. In discussing how personnel type, sort, stabilize, and nudge on behavior data, this paper examines the contingencies of data making processes and implications for the application of student data.
      Citation: Big Data & Society
      PubDate: 2020-06-16T05:48:56Z
      DOI: 10.1177/2053951720932200
      Issue No: Vol. 7, No. 1 (2020)
  • Perils of data-driven equity: Safety-net care and big data’s elusive
           grasp on health inequality

    • Authors: Taylor M Cruz
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Large-scale data systems are increasingly envisioned as tools for justice, with big data analytics offering a key opportunity to advance health equity. Health systems face growing public pressure to collect data on patient “social factors,” and advocates and public officials seek to leverage such data sources as a means of system transformation. Despite the promise of this “data-driven” strategy, there is little empirical work that examines big data in action directly within the sites of care expected to transform. In this article, I present a case study on one such initiative, focusing on a large public safety-net health system’s initiation of sexual orientation and gender identity (SOGI) data collection within the clinical setting. Drawing from ethnographic fieldwork and in-depth interviews with providers, staff, and administrators, I highlight three main challenges that elude big data’s grasp on inequality: (1) provider and staff’s limited understanding of the social significance of data collection; (2) patient perception of the cultural insensitivity of data items; and (3) clinic need to balance data requests with competing priorities within a constrained time window. These issues reflect structural challenges within safety-net care that big data alone are unable to address in advancing social justice. I discuss these findings by considering the present data-driven strategy alongside two complementary courses of action: diversifying the health professions workforce and clinical education reform. To truly advance justice, we need more than “just data”: we need to confront the fundamental conditions of social inequality.
      Citation: Big Data & Society
      PubDate: 2020-06-09T11:36:59Z
      DOI: 10.1177/2053951720928097
      Issue No: Vol. 7, No. 1 (2020)
  • The virtue of simplicity: On machine learning models in algorithmic

    • Authors: Kristian Bondo Hansen
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Machine learning models are becoming increasingly prevalent in algorithmic trading and investment management. The spread of machine learning in finance challenges existing practices of modelling and model use and creates a demand for practical solutions for how to manage the complexity pertaining to these techniques. Drawing on interviews with quants applying machine learning techniques to financial problems, the article examines how these people manage model complexity in the process of devising machine learning-powered trading algorithms. The analysis shows that machine learning quants use Ockham’s razor – things should not be multiplied without necessity – as a heuristic tool to prevent excess model complexity and secure a certain level of human control and interpretability in the modelling process. I argue that understanding the way quants handle the complexity of learning models is a key to grasping the transformation of the human’s role in contemporary data and model-driven finance. The study contributes to social studies of finance research on the human–model interplay by exploring it in the context of machine learning model use.
      Citation: Big Data & Society
      PubDate: 2020-05-20T11:54:17Z
      DOI: 10.1177/2053951720926558
      Issue No: Vol. 7, No. 1 (2020)
  • Big Data and surveillance: Hype, commercial logics and new intimate

    • Authors: Kirstie Ball, William Webster
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Big Data Analytics promises to help companies and public sector service providers anticipate consumer and service user behaviours so that they can be targeted in greater depth. The attempts made by these organisations to connect analytically with users raise questions about whether surveillance, and its associated ethical and rights-based concerns, are intensified. The articles in this special themed issue explore this question from both organisational and user perspectives. They highlight the hype which firms use to drive consumer, employee and service user engagement with analytics within both private and public spaces. Further, they explore extent to which, through Big Data, there is an attempt to expand surveillance into the emotional registers of domestic, embodied experience. Collectively, the papers reveal a fascinating nexus between the much-vaunted potential of analytics, the data practices themselves and the newly configured intimate spheres which have been drawn into the commercial value chain. Together, they highlight the need for conceptual and regulatory innovation so that analytics in practice may be better understood and critiqued. Whilst there is now a rich variety of scholarship on Big Data Analytics, critical perspectives on the organising practices of Big Data Analytics and its surveillance implications are thin on the ground. Combined, the articles published in this special theme begin to address this shortcoming.
      Citation: Big Data & Society
      PubDate: 2020-05-14T06:19:53Z
      DOI: 10.1177/2053951720925853
      Issue No: Vol. 7, No. 1 (2020)
  • Interpretation as luxury: Heart patients living with data doubt, hope, and

    • Authors: Stine Lomborg, Henriette Langstrup, Tariq Osman Andersen
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Personal health technologies such as apps and wearables that generate health and behavior data close to the individual patient are envisioned to enable personalized healthcare - and self-care. And yet, they are consumer devices. Proponents of these devices presuppose that measuring will be helpful, and that data will be meaningful. However, a growing body of research suggests that self-tracking data does not necessarily make sense to users. Drawing together data studies and digital health research, we aim to further research on data ambivalence, a term we use to refer to the ambiguities and uncertainties people experience when interpreting their own data, as well as the critical obligation towards cultivating ethically sound uses and responses to such data in context. We develop the relationship between data, interpretation, and context as a central theoretical and practical problem in the datafication of healthcare. We then show how interpretation and context matter for data ambivalence through an empirical study of heart patients with an implanted advanced pacemaker who were offered a Fitbit wristband for self-tracking as part of a research project. We argue that the hope, anxiety, and doubt connected to the promise and accuracy of data are tempered by the context and purpose of self-tracking, and by individual circumstances. Finally, we link the findings on context-sensitivity in data interpretation to questions about response-ability in cloud-based care infrastructures. We discuss the ethical dilemmas associated with the use of commercial wellness-technologies in healthcare, and with researching such emerging practices.
      Citation: Big Data & Society
      PubDate: 2020-05-08T08:20:40Z
      DOI: 10.1177/2053951720924436
      Issue No: Vol. 7, No. 1 (2020)
  • Folk theories of algorithmic recommendations on Spotify: Enacting data
           assemblages in the global South

    • Authors: Ignacio Siles, Andrés Segura-Castillo, Ricardo Solís, Mónica Sancho
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of “rich pictures” made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify (and conceives of it as a social being that provides recommendations thanks to surveillance) and another one that envisions it as a system full of resources (and a computational machine that offers an individualized musical experience through the appropriate kind of “training”). Whereas the first theory emphasizes local conceptions of social relations to make sense of algorithms, the second one stresses the role of algorithms in providing a global experience of music and technology. We analyze why people espouse either one of these theories (or both) and how these theories provide users with resources to enact different modalities of power and resistance in relation to recommendation algorithms. We argue that folk theories thus offer a productive way to broaden understanding of what agency means in relation to algorithms.
      Citation: Big Data & Society
      PubDate: 2020-04-30T06:01:13Z
      DOI: 10.1177/2053951720923377
      Issue No: Vol. 7, No. 1 (2020)
  • Expectations of artificial intelligence and the performativity of ethics:
           Implications for communication governance

    • Authors: Aphra Kerr, Marguerite Barry, John D Kelleher
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      This article draws on the sociology of expectations to examine the construction of expectations of ‘ethical AI’ and considers the implications of these expectations for communication governance. We first analyse a range of public documents to identify the key actors, mechanisms and issues which structure societal expectations around artificial intelligence (AI) and an emerging discourse on ethics. We then explore expectations of AI and ethics through a survey of members of the public. Finally, we discuss the implications of our findings for the role of AI in communication governance. We find that, despite societal expectations that we can design ethical AI, and public expectations that developers and governments should share responsibility for the outcomes of AI use, there is a significant divergence between these expectations and the ways in which AI technologies are currently used and governed in large scale communication systems. We conclude that discourses of ‘ethical AI’ are generically performative, but to become more effective we need to acknowledge the limitations of contemporary AI and the requirement for extensive human labour to meet the challenges of communication governance. An effective ethics of AI requires domain appropriate AI tools, updated professional practices, dignified places of work and robust regulatory and accountability frameworks.
      Citation: Big Data & Society
      PubDate: 2020-04-30T05:25:23Z
      DOI: 10.1177/2053951720915939
      Issue No: Vol. 7, No. 1 (2020)
  • A dialogic analysis of Hello Barbie’s conversations with children

    • Authors: Valerie Steeves
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      This paper analyses Hello Barbie as a commercial artefact to explore how big data practices are reshaping the enterprise of marketing. The doll uses voice recognition software to ‘listen’ to the child and ‘talk back’ by algorithmically selecting a response from 8000 predetermined lines of dialogue. As such, it is a useful example of how marketers use customer relationship management systems that rely on sophisticated data collection and analysis techniques to create a relationship between companies and customers in which both parties are positioned as active participants who are able to obtain what they wish from the interaction. I use dialogic analysis to see how Mattel ‘makes sense’ of the dialogue as a dialogic partner. I argue that, in spite of the rhetoric of instantaneity and personalization, in which the technology is positioned as an immediate response to a child’s imagination, Mattel’s dialogic communication is both asynchronous and carefully crafted to fit the child’s responses within predetermined consumer subjectivities that are crafted to encourage particular kinds of consumption. Although the dialogue spoken by Hello Barbie is able to situate Barbie as an active subject, the control exercised by the company in order to elicit data for customer relationship management purposes and steer the dialogue to brand-friendly messages relegates the child to a passive role. Accordingly, the doll fails to deliver the promises of customer relationship management.
      Citation: Big Data & Society
      PubDate: 2020-04-29T07:18:51Z
      DOI: 10.1177/2053951720919151
      Issue No: Vol. 7, No. 1 (2020)
  • Playing with machines: Using machine learning to understand automated
           copyright enforcement at scale

    • Authors: Joanne E Gray, Nicolas P Suzor
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-28T06:24:07Z
      DOI: 10.1177/2053951720919963
      Issue No: Vol. 7, No. 1 (2020)
  • The trainer, the verifier, the imitator: Three ways in which human
           platform workers support artificial intelligence

    • Authors: Paola Tubaro, Antonio A Casilli, Marion Coville
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-24T07:49:38Z
      DOI: 10.1177/2053951720919776
      Issue No: Vol. 7, No. 1 (2020)
  • No amount of “AI” in content moderation will solve filtering’s
           prior-restraint problem

    • Authors: Emma J Llansó
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.
      Contemporary policy debates about managing the enormous volume of online content have taken a renewed focus on upload filtering, automated detection of potentially illegal content, and other “proactive measures”. Often, policymakers and tech industry players invoke artificial intelligence as the solution to complex challenges around online content, promising that AI is a scant few years away from resolving everything from hate speech to harassment to the spread of terrorist propaganda. Missing from these promises, however, is an acknowledgement that proactive identification and automated removal of user-generated content raises problems beyond issues of “accuracy” and overbreadth--problems that will not be solved with more sophisticated AI. In this commentary, I discuss how the technical realities of content filtering stack up against the protections for freedom of expression in international human rights law. As policymakers and companies around the world turn to AI for communications governance, it is crucial that we recall why legal protections for speech have included presumptions against prior censorship, and consider carefully how proactive content moderation will fundamentally re-shape the relationship between rules, people, and their speech.
      Citation: Big Data & Society
      PubDate: 2020-04-23T08:50:15Z
      DOI: 10.1177/2053951720920686
      Issue No: Vol. 7, No. 1 (2020)
  • Everyday curation' Attending to data, records and record keeping in
           the practices of self-monitoring

    • Authors: Kate Weiner, Catherine Will, Flis Henwood, Rosalind Williams
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-22T08:26:28Z
      DOI: 10.1177/2053951720918275
      Issue No: Vol. 7, No. 1 (2020)
  • How to translate artificial intelligence' Myths and justifications in
           public discourse

    • Authors: Jonathan Roberge, Marius Senneville, Kevin Morin
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-17T06:58:31Z
      DOI: 10.1177/2053951720919968
      Issue No: Vol. 7, No. 1 (2020)
  • Evolving data teams: Tensions between organisational structure and
           professional subculture

    • Authors: Florian Stalph
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-17T06:53:29Z
      DOI: 10.1177/2053951720919964
      Issue No: Vol. 7, No. 1 (2020)
  • Plastic surveillance: Payment cards and the history of transactional data,
           1888 to present

    • Authors: Josh Lauer
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-03T06:55:41Z
      DOI: 10.1177/2053951720907632
      Issue No: Vol. 7, No. 1 (2020)
  • ‘Happy failures’: Experimentation with behaviour-based
           personalisation in car insurance

    • Authors: Gert Meyers, Ine Van Hoyweghen
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-01T11:01:00Z
      DOI: 10.1177/2053951720914650
      Issue No: Vol. 7, No. 1 (2020)
  • The value of Big Data in government: The case of ‘smart

    • Authors: Karl Löfgren, C William R Webster
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-04-01T03:50:13Z
      DOI: 10.1177/2053951720912775
      Issue No: Vol. 7, No. 1 (2020)
  • Emotional AI, soft biometrics and the surveillance of emotional life: An
           unusual consensus on privacy

    • Authors: Andrew McStay
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-03-27T08:02:17Z
      DOI: 10.1177/2053951720904386
      Issue No: Vol. 7, No. 1 (2020)
  • Data as performance – Showcasing cities through open data maps

    • Authors: Morgan Currie
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-03-25T08:30:24Z
      DOI: 10.1177/2053951720907953
      Issue No: Vol. 7, No. 1 (2020)
  • Beyond algorithmic reformism: Forward engineering the designs of
           algorithmic systems

    • Authors: Peter Polack
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-03-20T11:07:44Z
      DOI: 10.1177/2053951720913064
      Issue No: Vol. 7, No. 1 (2020)
  • Establishing a social licence for Financial Technology: Reflections on the
           role of the private sector in pursuing ethical data practices

    • Authors: Mhairi Aitken, Ehsan Toreini, Peter Carmichael, Kovila Coopamootoo, Karen Elliott, Aad van Moorsel
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-03-04T12:24:34Z
      DOI: 10.1177/2053951720908892
      Issue No: Vol. 7, No. 1 (2020)
  • Algorithmic content moderation: Technical and political challenges in the
           automation of platform governance

    • Authors: Robert Gorwa, Reuben Binns, Christian Katzenbach
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-02-28T11:48:19Z
      DOI: 10.1177/2053951719897945
      Issue No: Vol. 7, No. 1 (2020)
  • The old in the new: Voter surveillance in political clientelism and
           datafied campaigning

    • Authors: Isabel Kusche
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-02-24T10:05:52Z
      DOI: 10.1177/2053951720908290
      Issue No: Vol. 7, No. 1 (2020)
  • The Nordic data imaginary

    • Authors: Aaro Tupasela, Karoliina Snell, Heta Tarkkala
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-02-20T11:11:37Z
      DOI: 10.1177/2053951720907107
      Issue No: Vol. 7, No. 1 (2020)
  • Prospecting (in) the data sciences

    • Authors: Stephen C Slota, Andrew S Hoffman, David Ribes, Geoffrey C Bowker
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-02-18T12:02:23Z
      DOI: 10.1177/2053951720906849
      Issue No: Vol. 7, No. 1 (2020)
  • How biased is the sample' Reverse engineering the ranking algorithm of
           Facebook’s Graph application programming interface

    • Authors: Justin Chun-Ting Ho
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-02-17T09:50:34Z
      DOI: 10.1177/2053951720905874
      Issue No: Vol. 7, No. 1 (2020)
  • Smart forests and data practices: From the Internet of Trees to planetary

    • Authors: Jennifer Gabrys
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-02-14T08:54:31Z
      DOI: 10.1177/2053951720904871
      Issue No: Vol. 7, No. 1 (2020)
  • Manipulate to empower: Hyper-relevance and the contradictions of marketing
           in the age of surveillance capitalism

    • Authors: Aron Darmody, Detlev Zwick
      Abstract: Big Data & Society, Volume 7, Issue 1, January-June 2020.

      Citation: Big Data & Society
      PubDate: 2020-02-05T06:53:17Z
      DOI: 10.1177/2053951720904112
      Issue No: Vol. 7, No. 1 (2020)
  • A Maussian bargain: Accumulation by gift in the digital economy

    • Authors: Marion Fourcade, Daniel N Kluttz
      Abstract: Big Data & Society, Volume 7, Issue 1, July-December 2019.

      Citation: Big Data & Society
      PubDate: 2020-02-03T11:47:37Z
      DOI: 10.1177/2053951719897092
      Issue No: Vol. 7, No. 1 (2020)
  • Personal choices and situated data: Privacy negotiations and the
           acceptance of household Intelligent Personal Assistants

    • Authors: Jason Pridmore, Anouk Mols
      Abstract: Big Data & Society, Volume 7, Issue 1, July-December 2019.

      Citation: Big Data & Society
      PubDate: 2020-01-31T11:30:20Z
      DOI: 10.1177/2053951719891748
      Issue No: Vol. 7, No. 1 (2020)
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