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Editorial introduction: Towards a machinic anthropology

Editorial introduction: Towards a machinic anthropology Bringing together a motley crew of social scientists and data scientists, the aim of this special theme issue is to explore what an integration or even fusion between anthropology and data science might look like. Going beyond existing work on the complementarity between ‘thick’ qualitative and ‘big’ quantitative data, the ambition is to unsettle and push established disciplinary, methodological and epistemological boundaries by creatively and critically probing various computational methods for augmenting and automatizing the collection, processing and analysis of ethnographic data, and vice versa. Can ethnographic and other qualitative data and methods be integrated with natural language processing tools and other machine-learning techniques, and if so, to what effect? Does the rise of data science allow for the realization of Levi-Strauss’ old dream of a computational structuralism, and even if so, should it? Might one even go as far as saying that computers are now becoming agents of social scientific analysis or even thinking: are we about to witness the birth of distinctly anthropological forms of artificial intelligence? By exploring these questions, the hope is not only to introduce scholars and students to computational anthropological methods, but also to disrupt predominant norms and assumptions among computational social scientists and data science writ large. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Big Data & Society SAGE

Editorial introduction: Towards a machinic anthropology

Pedersen, Morten Axel
Big Data & Society , Volume 10 (1): 1 – Jan 1, 2023
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Publisher
SAGE
Copyright
© The Author(s) 2023
ISSN
2053-9517
eISSN
2053-9517
DOI
10.1177/20539517231153803
Publisher site
See Article on Publisher Site

Abstract

Bringing together a motley crew of social scientists and data scientists, the aim of this special theme issue is to explore what an integration or even fusion between anthropology and data science might look like. Going beyond existing work on the complementarity between ‘thick’ qualitative and ‘big’ quantitative data, the ambition is to unsettle and push established disciplinary, methodological and epistemological boundaries by creatively and critically probing various computational methods for augmenting and automatizing the collection, processing and analysis of ethnographic data, and vice versa. Can ethnographic and other qualitative data and methods be integrated with natural language processing tools and other machine-learning techniques, and if so, to what effect? Does the rise of data science allow for the realization of Levi-Strauss’ old dream of a computational structuralism, and even if so, should it? Might one even go as far as saying that computers are now becoming agents of social scientific analysis or even thinking: are we about to witness the birth of distinctly anthropological forms of artificial intelligence? By exploring these questions, the hope is not only to introduce scholars and students to computational anthropological methods, but also to disrupt predominant norms and assumptions among computational social scientists and data science writ large.

Journal

Big Data & Society – SAGE

Published: Jan 1, 2023

Keywords: Computational ethnography; quali-quantitative methods; mixed methods; digital methods; social data science; machine anthropology

References

  • The promises of computational ethnography: Improving transparency, replicability, and validity for realist approaches to ethnographic analysis
    Abramson, CM; Joslyn, J; Rendle, KA
  • Measurement validity: A shared standard for qualitative and quantitative research
    Adcock, R; Collier, D
  • An ethnography by any other name
    Agar, M
  • The end of theory: The data deluge makes the scientific method obsolete
    Anderson, C
  • Commentary: Why (and how to) experiment with digital social data?
    Blok, A
  • Stitching together the heterogeneous party: A complementary social data science experiment
    Blok, A; Carlsen, HB; Jørgensen, TB
  • Complementary social science? Quali-quantitative experiments in a big data world
    Blok, A; Pedersen, MA
  • Big–thick blending: A method for mixing analytical insights from big and thick data sources
    Bornakke, T; Due, BL
  • Abductive logic of inquiry for quantitative research in the digital age
    Brandt, P; Timmermans, S
  • Capturing distinctions while mining text data: Toward low-tech formalization for text analysis
    Breiger, RL; Wagner-Pacifici, R; Mohr, JW
  • “Affective publics” performing trust on Danish Twitter during the COVID-19 lockdown
    Breslin, S; Blok, A; Enggaard, TR
  • My unexpectedly militant bots: A case for programming-as-social-science
    Brooker, P
  • Machine learning: A structuralist discipline?
    Bruchansky, C
  • Quantification and statistics in six anthropology journals
    Chibnik, M
  • The ethnographer and the algorithm: Beyond the black box
    Christin, A
  • Experiencing presence: An interactive model of perception
    Corwin, AI; Erickson-Davis, C
  • Construct validity in psychological tests
    Cronbach, LJ; Meehl, PE
  • Machine learning applications in anthropology: Automated discovery over kinship structures
    Cunningham, SJ
  • Symmetry and entropy: Mathematical metaphors in the work of Levi-Strauss [and comments and reply]
    de Almeida, MWB; Arcand, B; Jorion, P
  • Adapting computational text analysis to social science (and vice versa)
    DiMaggio, P
  • Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding
    DiMaggio, P; Nag, M; Blei, D
  • Introduction: Towards an anthropology of data
    Douglas-Jones, R; Walford, A; Seaver, N
  • Machine translation: Mining text for social theory
    Evans, JA; Aceves, P
  • Big data and ethnology
    Feldman, J
  • Triangulation and mixed methods designs: Data integration with new research technologies
    Fielding, NG
  • Big data and small: Collaborations between ethnographers and data scientists
    Ford, H
  • Relating social and symbolic relations in quantitative text analysis. A study of parliamentary discourse in the Weimar Republic
    Fuhse, J; Stuhler, O; Riebling, J
  • Text as data
    Gentzkow, M; Kelly, B; Taddy, M
  • Ethnographic data in British social anthropology
    Gluckman, M
  • Predicting consumer behavior with web search
    Goel, S; Hofman, JM; Lahaie, S
  • The data revolution in social science needs qualitative research
    Grigoropoulou, N; Small, ML
  • Machine learning for social science: An agnostic approach
    Grimmer, J; Roberts, ME; Stewart, BM
  • Text as data: The promise and pitfalls of automatic content analysis methods for political texts
    Grimmer, J; Stewart, BM
  • Beyond platform capitalism: Critical perspectives on Facebook markets from Melanesia
    Hobbis, G; Hobbis, SK
  • Digital ethnography toward augmented empiricism: A new methodological framework
    Hsu, WF
  • That’s enough about ethnography!
    Ingold, T
  • Big data, new epistemologies and paradigm shifts
    Kitchin, R
  • The epistemic and performative dynamics of machine learning praxis
    Kockelman, P
  • The geometry of culture: Analyzing the meanings of class through word embeddings
    Kozlowski, AC; Taddy, M; Evans, JA
  • Meaningful measures of human society in the twenty-first century
    Lazer, D; Hargittai, E; Freelon, D
  • Computational social science
    Lazer, D; Pentland, A; Adamic, L
  • Life in the network: The coming age of computational social science
    Lazer, D; Pentland, A; Adamic, L
  • Algorithms & automation: An introduction
    Lowrie, I
  • On the importance of ethnographic methods in AI research
    Marda, V; Narayan, S
  • Sociology in the era of big data: The ascent of forensic social science
    McFarland, DA; Lewis, K; Goldberg, A
  • Newton didn’t frame hypotheses. Why should we
    Milner, ST
  • Rethinking the ‘Great Divide’: Approaching interdisciplinary collaborations around digital data with humour and irony
    Moats, D
  • Quali-quantitative methods beyond networks: Studying information diffusion on twitter with the modulation sequencer
    Moats, D; Borra, E
  • “You social scientists love mind games”: Experimenting in the “divide” between data science and critical algorithm studies
    Moats, D; Seaver, N
  • Special issue title: Topic models and the cultural sciences
    Mohr, JW; Bogdanov, P
  • Formal methods of cultural analysis
    Mohr, JW; Rawlings, C
  • Toward a computational hermeneutics
    Mohr, JW; Wagner-Pacifici, R; Breiger, RL
  • Four styles of quali-quantitative analysis: Making sense of the new nordic food movement on the web
    Munk, AK
  • Computational grounded theory: A methodological framework
    Nelson, LK
  • The future of coding: A comparison of hand-coding and three types of computer-assisted text analysis methods
    Nelson, LK; Burk, D; Knudsen, M
  • Anthropology by data science
    Paff, S
  • Anthropology of/as repetition
    Pedersen, MA
  • Data mining workspace sensors
    Pretnar, A; Podjed, D
  • Theory in, theory out: The uses of social theory in machine learning for social science
    Radford, J; Joseph, K
  • Tracing medicinal agencies: Antidepressants and life-effects
    Ruckenstein, M
  • Reassembling social science methods: The challenge of digital devices
    Ruppert, E; Law, J; Savage, M
  • A discrete-event simulation of Claude Lévi-Strauss’ structural analysis of myths based on symmetry and double twist transformations
    Santucci, J-F; Doja, A; Capocchi, L
  • Algorithms as culture: Some tactics for the ethnography of algorithmic systems
    Seaver, N
  • Fundamental structures of dynamic social networks
    Sekara, V; Stopczynski, A; Lehmann, S
  • To explain or to predict?
    Shmueli, G
  • A not quite random walk: Experimenting with the ethnomethods of the algorithm
    Ziewitz, M