Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Complementary social science? Quali-quantitative experiments in a Big Data world:

Complementary social science? Quali-quantitative experiments in a Big Data world: The rise of Big Data in the social realm poses significant questions at the intersection of science, technology, and society, including in terms of how new large-scale social databases are currently changing the methods, epistemologies, and politics of social science. In this commentary, we address such epochal (‘‘large-scale’’) questions by way of a (situated) experiment: at the Danish Technical University in Copenhagen, an interdisciplinary group of computer scientists, physi- cists, economists, sociologists, and anthropologists (including the authors) is setting up a large-scale data infrastructure, meant to continually record the digital traces of social relations among an entire freshman class of students (N> 1000). At the same time, fieldwork is carried out on friendship (and other) relations amongst the same group of students. On this basis, the question we pose is the following: what kind of knowledge is obtained on this social micro-cosmos via the Big (computational, quantitative) and Small (embodied, qualitative) Data, respectively? How do the two relate? Invoking Bohr’s principle of complementarity as analogy, we hypothesize that social relations, as objects of knowledge, depend crucially on the type of measurement device deployed. At the same time, however, we also expect new interferences and polyphonies to arise at the intersection of Big and Small Data, provided that these are, so to speak, mixed with care. These questions, we stress, are important not only for the future of social science methods but also for the type of societal (self-)knowledge that may be expected from new large-scale social databases. Keywords Principle of complementarity, method devices, quali-quantitative methods, social science experiments, computational social science, Big Data critique Introduction of complementarity’’ be brought to bear on current interdisciplinary research fields in the social sciences, The information regarding the behaviour of one and the which comprise both quantitative and qualitative meth- same object under mutually exclusive experimental set- ods and data-sets? Indeed, may Bohr’s far-reaching tings may .. . , in an often-used expression within atomic thinking inspire us to conjure a new epistemology as physics, suitably be characterized as complementary, in well as a new politics of ‘‘quali-quantitative’’ (Latour that – although their description in everyday language et al., 2012) methods for the social sciences? cannot be subsumed into one whole – they nevertheless each express equally important aspects of the total sum Department of Sociology, University of Copenhagen, Copenhagen, of thinkable experiences regarding the object. (Bohr, Denmark 1957 [1938]: 38; authors’ translation) Department of Anthropology, University of Copenhagen, Copenhagen, Denmark Such were the words of the renowned physicist Niels Corresponding author: Bohr (1885–1962) in a talk he delivered in 1938 to an Anders Blok, Department of Sociology, Øster Farimagsgade 5, audience of Danish anthropologists. We cite this pas- Copenhagen K, 1014 Denmark. sage because we want to ask: how can Bohr’s ‘‘principle Email: abl@soc.ku.dk Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http:// www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (http://www.uk.sagepub.com/aboutus/open- access.htm). 2 Big Data & Society It is important to note that, while Bohr was and still interferences may be expected to arise at the intersec- today is hailed for his contribution to modern physics, tion of Big and Small Data, provided that these are, so ‘‘[t]he full radical nature of Bohr’s views has not always to speak, mixed with care. been recognized’’ by physicists (Pinch, 2011: 6–7). Below, we explore such questions by way of discuss- Indeed, within both the natural and the social sciences, ing a specific social science experiment, which attempts there is widespread skepticism if not downright hostility to mix computational and ethnographic methods towards bringing together radically different research into what might be dubbed ‘‘Complementary Social methods and traditions in the hope of generating new Science,’’ namely the Copenhagen Social Networks and better insights into complex phenomena. The cross- Study. In this research project, an interdisciplinary disciplinary field of ‘‘computational social science’’ is a group of computer scientists, physicists, economists, case in point. Consider the following two citations, sociologists and anthropologists (including our- taken from the agenda-setting 2009 Science paper co- selves) has recently embarked on a novel attempt to authored by leading researchers from this nascent field, combine quantitative width and qualitative depth in and from a recent column in Huffington Post by US mapping the spatio-temporal details of a concrete anthropologist Paul Stoller (2013): social network. In short, a computational social science is emerging Social fabric: Remixing mixed methods that leverages the capacity to collect and analyze data with an unprecedented breadth and depth and scale. The Copenhagen Social Networks Study is a large-scale (Lazer et al., 2009: 722) cross-disciplinary research program jointly hosted by the Faculty of Social Sciences, University of Copenhagen, The problem of Big Data is here to stay, which means and the Department of Informatics and Mathematical that in the coming months and years we’ll need a legion Modelling at the Danish Technical University (DTU). of ethnographically trained analysts to produce ‘Thick Our 25-plus member big team collaborates to make con- Data’ – to save us from ourselves. (Stoller, 2013) tinuous recordings of social interactions at all commu- nication channels among an entire freshman class Lazer et al. (2009), of course, echo broader visions (N> 1000) of DTU students, using smart phones distrib- (in)famously made on behalf of the scientific ‘‘Big uted to students (as well as to members of the project Data revolution’’ such as Chris Anderson’s controver- group) as measurement devices (‘‘socio-meters’’). This sial claim about a supposed ‘‘end of theory’’ (2008). On allows us to digitally map out the ‘‘complete’’ social net- his part, Stoller draws heavily on a blog posting by work of an entire freshman class, including face-to-face Tricia Wang (2013), who argues that ‘‘ethnographers encounters via Bluetooth, geo-location proximities via must engage with Big Data,’’ for fear of being ‘‘mini- GPS, social network data via apps, and telecommunica- mized as a small line item on a budget, and relegated to tion data via call logs. The set-up also includes, by way the small data corner.’’ of ‘‘embedding’’ an anthropologist within the freshmen Our ambition in this piece is to sketch an alternative group for an entire year, ‘‘thick’’ ethnographic fieldwork to these two mutually exclusive (and mutually hostile) data on friendship and other social relations amongst the understandings of the role and value of (seemingly) same group of students. Simultaneously, researchers ‘‘big’’ quantitative data and (seemingly) ‘‘small’’ quali- track different components of the social fabric via the tative data methods and approaches in contemporary application of established survey methods (for an over- research and society. To do so, we pose the following view, see Stopczynski et al., 2014). This vast and hetero- questions: how do the kinds of knowledge that can be geneous collection of data on a large-scale social obtained from ‘‘Big’’ (computational) and ‘‘Small’’ network infrastructure is being used to study: (ethnographic) data, respectively, relate or not relate? Indeed, beyond the suggestion of Stoller and ‘‘like- (i) How information and influence are transmitted minded’’ social scientists voicing tout court concern and transformed in the DTU ‘‘social fabric’’ about Big Data, might the notion of thick data (ii) How friendships, networks and behaviors form, (c.f. op cit; see also Boellstorff, 2013) be put to better offline and online use to denote what relates, rather than what separates, (iii) How the researchers themselves study ‘‘Big Data’’ and handle issues of ethics and privacy innovative approaches to future social science experi- ments? Invoking Bohr’s principle of complementarity, we contend that social relations, as objects of both sci- Within this framework, a team of sociologists and entific and everyday knowledge, indeed depend on the anthropologists (senior scholars, doctoral students, and type of measurement device deployed for their observa- undergraduates) from Copenhagen University, led by tion. At the same time, we propose that new productive the two of us, conduct a joint ‘‘Ant-Soc’’ work package, Blok and Pedersen 3 which aims to push current boundaries for mixing and (i.e. ethnographic) data approaches in light of our cross-fertilizing quantitatively and qualitatively based experiences from the Copenhagen Social Networks social network research. We do this by exploring a Study project so far? To begin with, the collaborative number of closely interrelated research questions, research vision we advocate here differs from the hostile themes and methods at the core of current concerns stance towards Big Data by increasing numbers of in sociology, anthropology, and science and technology qualitative social scientists. Valuable as the emerging studies (STS) – as well as in the cross-disciplinary fields Critical Big Data Studies Paradigm (as one might call of computational, digital, and experimental social sci- it) undoubtedly will prove to be, it tacitly relies upon ence – including: and thus reproduces a problematic bifurcation between ‘‘hard’’ quantitative evidence in need of further inter- . Ethnographic fieldwork – how can ethnographic stu- pretation and ‘‘soft’’ qualitative data imbued with ‘‘the dies of friendship and other social network relations meaning’’ needed to close this hermeneutic gap. amongst students enrich or challenge computational As Boyd and Crawford (2012: 670) write, echoing approaches – and vice versa? Stoller’s and Wang’s positions: . Quali-quantitative methods – does the rise of com- putational social science lead to a reconfiguration of [I]t is increasingly important to recognize the value of traditional splits between quantitative and qualita- ‘small data’. [...] Take, for example, the work of Veinot tive research methods? (2007), who followed one worker – a vault inspector at . Big data experiments – what kinds of anthropo- a hydroelectric utility company – in order to under- logical and sociological experiments does our set- stand the information practices of a blue-collar up enable, and how might such innovations enrich worker. [.. .] Her work tells a story that could not be existing social-scientific designs? discovered by farming millions of Facebook or Twitter . Social life of Big Data – what new ethical, political accounts. and organizational challenges and opportunities does the rise of large-scale social databases pose to While we very much sympathize with Boyd and the social sciences and society at large? Crawford’s ambition to affirm the lasting value of eth- . Research collaboration – taken as an object of STS, nography and its critical potential, arguments such as what may be learned about cross-disciplinary collab- the above also tend to reproduce prevailing assump- oration from the research program itself? tions that ‘‘big’’ and ‘‘small’’ data and methods are simply mutually exclusive. This runs counter to what In terms of social science methods, the unique cross- we would like to think the Copenhagen Social disciplinary and cross-institutional set-up of the Networks Study project allows for, which is to make Copenhagen Social Networks Study allows for drawing ‘‘big’’ and ‘‘small’’ data mutually dependent and enhan- together two hitherto distinct literatures, on computa- cing, and thus potentially recalibrate this and other tional social science (e.g. Lazer et al., 2009) and mixed unhelpful bifurcations between so-called quantitative methods (e.g. Creswell, 2011), respectively. To the best and so-called qualitative data worlds. That, after all, of our knowledge, researchers in computational social is what complementarity in Bohr’s definition was all science are yet to acknowledge the potential significance about: an epistemological and ontological predicament, to their work of grounded participant-observation in whereby two phenomena or processes are at one and the ethnographic tradition. Conversely, the mixed the same time totally disparate and totally interdepend- methods literature so far has not picked up the chal- ent. As Karen Barad (2011: 444) puts the point: lenges and opportunities of large-scale digital data, and ‘‘Complementarity entails two important features: is therefore at risk of remaining trapped within an mutual exclusivity and mutual necessity. For two vari- obsolete distinction between qualitative and quantita- ables to be complementary they have to be both simul- tive (i.e. survey-based) approaches. This methodo- taneously necessary and mutually exclusive. Otherwise, logical remixing – which allows for new ways of what is the paradox?’’. Critical Big Data studies, it stitching together computational and ethnographic seems to us, risk losing the paradox of complementarity data – is what we propose to dub ‘‘complementary rather than benefitting from it. social science.’’ Another problem with such an emerging ‘‘critical consensus’’ amongst qualitative social scientists is its overly narrow understanding of what critique is. Bohr revisited: A complementary Put bluntly, there is something unsatisfactory social science? about reducing the intervention that sociologists and What can we say about the relationship between ‘‘big’’ anthropologists like us can make on Big Data realities or ‘‘deep’’ (i.e. computational) and ‘‘small’’ or ‘‘thick’’ and discourses to a one-dimensional question of 4 Big Data & Society deconstruction and debunking. Again, we are of course reconfiguring the very epistemological, methodological, not claiming that allegedly ‘‘neutral’’ incipient hege- and political playing field onto which the recently monic discourses on, e.g., so-called non-theoretical much-hyped discourses of ‘‘Big Data’’ social science computational social science can and should not be make claim. As an attempt to work around and thus questioned. All we are suggesting is that too much distort what is on the inside and what is on the outside default Big Data bashing runs the risk of tacitly relying of various conventionalized bifurcations and contrasts on an assumed vantage point from outside these dis- (researcher vs. research object; natural science vs. social courses and practices – a quite conservative observa- science/humanities; ‘‘hard’’ quantitative vs. ‘‘soft’’ tional ‘‘fly on the wall’’ position from which the qualitative data and approaches), our vision of a com- anthropologist and sociologist are taken to enjoy plementary social science contains the promise of an unique access to both her object of study and a broader ‘‘immanent,’’ non-skeptical critique (cf. Holbraad ‘‘context’’ of which it is supposedly a ‘‘part’’ (cf. Riles, et al., 2013) of narrowly technical and neo-positivist 1998). Take, for instance, Mike Savage and Roger celebrations of Big Data in research as well as non- Burrows’s (2007) otherwise pertinent points about research contexts. what they (following Nigel Thrift) dub ‘‘knowing cap- italism’’; surely, we might counter, there is more to the Quali-quantitative methods Big Data challenge than merely diagnosing a new form of capitalism? In a recent article aptly entitled ‘‘The whole is always In the Copenhagen Social Networks Study project, smaller than its parts,’’ Latour et al. ask: adopting such a conservative notion of critique would entail that physicists like Sune Lehman (the PI from [...] [I]s there a way to define what is a longer lasting DTU) were located fully within the large-scale social social order without making the assumption that there network database investigated by our interdisciplinary exist two levels [of individuals and structures]. [...] team, whereas sociologists and anthropologists, such as Instead of having to choose and thus to jump from the two of us, would be positioned on the outside of this individuals to wholes, from micro to macro, [we want Big Data reality and discourse. All we would do (or to] occupy all sorts of other positions, constantly rear- pretend to be doing) as so-called critical anthropolo- ranging the way profiles are interconnected and over- gists and sociologists, then, would be to observe pur- lapping. (2012: 591) portedly ‘‘native’’ computational social scientists by looking at them from the outside in, seeking to identify, To a large extent, this passage captures what the two of monitor, trace, describe and ultimately decode the more us hope to achieve from our quest to laterally assemble or less exotic ideas and practices of this scientific into a single research reality (or ‘‘research ‘‘tribe.’’ Yet, exciting and self-satisfying as this would relationality’’) the two hitherto bifurcated social scien- undoubtedly be, we would thereby end up replicating tific arenas of Big and Small data: by stubbornly resist- the dubious assumptions about mutual exclusivity ing the temptation to perceive the cross-disciplinary between quantitative Big Data and qualitative small Copenhagen Social Networks Study as comprised by data social science that we set out to transcend in the two mutually exclusive methodological and first place. epistemological domains – a quantitative and a Our vision of a complementary social science is qualitative one, respectively – we wish to insist on the meant to offer a way of avoiding such pitfalls. By potential for new and progressive forms of what Latour attempting to mix and even merge our own research et al. (2012) dub ‘‘quali-quantitative methods.’’ This, agendas, perspectives, and methodologies with our however, raises a new order of urgent questions. ‘‘native’’ computational social scientific ‘‘informants,’’ It is becoming increasingly clear that, within algo- we have from the onset of the project attempted to rithmically generated Big Data worlds such as the position ourselves within the large-scale social network digital database generated in our research program, database under investigation, not on the outside of it – the ‘‘part’’ is indeed often bigger than the ‘‘whole,’’ as as would be the aspiration of many conventional Latour et al. suggest. This is illustrated, for instance, by science studies approaches. For only by in this way the way in which complexity in quantitative social net- strategically striving to partly collapse our own work mappings tends to increase, as opposed research interests with those of the other researchers to decrease, the moment one looks not for ‘‘aggregate’’ involved in the Copenhagen Social Networks Study static structures but for the replication, say, of ever can we hope to move from mutual exclusivity to inter- more fine-grained ‘‘temporal motifs’’ in dynamic inter- dependency in the complementarity between computa- action patterns across smaller groups (Kovanen et al., tional and ethnographic approaches. We like to think 2011). But are such granularities necessarily the same in of this as a germane strategy for gradually Big and Small data worlds? Blok and Pedersen 5 By investing so heavily in the promises of new large- can we quantify the importance of ‘‘ambience,’’ ‘‘atmos- scale ‘‘digital trace’’ databases, Latour et al. may risk phere,’’ ‘‘togetherness,’’ and so on in this respect – losing sight of the ways in which disparate data worlds ‘‘quali-quantities’’ which, from a standard sociological rub off against and emerge from each other, rather than and anthropological point of view, would be purely producing new seamless ‘‘wholes.’’ Within computa- qualitative? What, in turn, might such new data assem- tional social science, for instance, the focus on granu- blages that ‘‘stitch together’’ data worlds produced larity ‘‘drives forward a concern with the microscopic, through computational and ethnographic methods do the way that amalgamations of databases can allow to established concepts of ‘‘personhood,’’ ‘‘sociality,’’ ever more granular, unique, specification’’ and ‘‘politics’’? (Ruppert et al., 2013: 38). Accordingly, in the Copenhagen Social Networks Study project, we hope Conclusion to extend the method of quali-quantitative research by making the very study of social relations – by means of We are well aware that our vision of a complementary ethnographic fieldwork, surveys, digital traces, and so social science for the 21st century raises many new on – part and parcel of the experimental set-up itself. questions and numerous potential objections. Apart By doing so, we put ourselves in a position to experi- from the sheer ‘‘technical’’ challenges of remixing dif- ment on social science complementarity in practice: ferent methods, devices, infrastructures, and data forms how, we ask, may our experimental settings of obser- (ranging from ethnographic field notes to database vation themselves be experimentally varied so as to algorithms) into ‘‘thick’’ data, questions arise at the create different kinds of mutual necessity between Big heart of the philosophy of science. For example: if it (computational) and Small (ethnographic) data? What is indeed the case that, in computational social science, forms of ‘‘granularity,’’ ‘‘thickness,’’ and ‘‘depth’’ arise we leave behind the search for causal statistical model- from engaging in, rather than simply tracing, concrete ing and enter a new world of visual ‘‘pattern recogni- work of computational data design, compilation and tion’’ (Ruppert et al., 2013: 36), then what happens to assemblage, alongside ethnographic descriptions (see time-honored distinctions between numbers and narra- also Kockelman, 2013)? tives; description and explanation; and indeed, simula- In pursuing this kind of experimental approach, new tion and the real world? Such questions, it seems to us, and difficult questions of research ethics emerge, in part are not just becoming ever more pertinent with ongoing because established conventions on how to deal respon- digital social database developments (including recent sibly with issues of privacy, confidentiality, etc., differ scares and scandals). They can also only be addressed widely between (and to some degree also within) com- in the same ‘‘messy’’ interface between basic research, putational and ethnographic approaches. Here as well, social and political engagement, and collaborative however, much-needed dialogues and imports should experimentation, which Niels Bohr pioneered in go both ways, as computational researchers stand to his time. learn from routine experiences, on the part of field- Furthermore, as noted, our vision of a complemen- workers, of gaining access to ‘‘private’’ layers of peo- tary social science must respond to legitimate and ple’s lives far beyond what should ever be conveyed in urgent ethical and political concerns – raised by both ‘‘public’’ research. We need to ask: what could ethically critics and supporters of computational data – with appropriate, and indeed ethically desirable, forms of regard to issues of surveillance, privacy, and future complementary social science data be? misuse of data. Once again, without claiming to have At this relatively early stage of our collective endea- in any way ‘‘solved’’ these issues, the Copenhagen vors, it is still premature to give definite answers to such Social Networks Study team has already taken some epistemological and ethical questions. What we can important steps in trying to make data available to provide, however, is a flavor of what we have in the research subjects themselves via apps and websites mind. For example: how might the combination of (including the official project website: https:// ethnographic embedding and computational data help www.sensible.dtu.dk/), as well as more interactive on- us understand the way various affective moods, and offline forums, and by including members of the impulses, and rumors spread throughout a collectivity? project team themselves in the experiment. In this sense, As Ruppert et al. (2013: 35) suggest, the transactional our notion of complementarity extends to the mutual data favored by most computational social science dependencies of researchers and their subjects; to the lends itself to entirely ‘‘non-individualist’’ accounts of greatest extent possible, research subjects should be cast social life, where the play of fluid and dynamic trans- as co-producers of knowledge about themselves, just as actions is the focus of attention. In our setting, one researchers should strive whenever feasible to render question might be: what role does parties and partying themselves subject to their own research questions play in the formation of social connections, and how and methods. Our collaborative research program, 6 Big Data & Society Boyd D and Crawford K (2012) Critical questions for big then, is not just about methods and results; it is also, data. Information, Communication & Society 15(5): more broadly or ‘‘thickly,’’ an experiment in ‘‘data 662–679. democracy.’’ Creswell JW (2011) Controversies in mixed methods research. In: Denzin NK and Lincoln YS (eds) The SAGE Declaration of conflicting interests Handbook of Qualitative Research. 4th ed. London: The author declares that there is no conflict of interest. SAGE Publications, pp. 269–283. Holbraad M, Pedersen MA and Viveiros de Castro E (2013) Funding The politics of ontology: Anthropological positions. (Theorizing the contemporary). Cultural Anthropology. This research received no specific grant from any funding Available at: www.culanth.org/fieldsights/462-the- agency in the public, commercial, or not-for-profit sectors. politics-of-ontology-anthropological-positions (accessed 7 July 2014). Acknowledgements Kockelman P (2013) The anthropology of an equation. A first version of this text was presented at the University of Sieves, spam filters, agentive algorithms, and ontologies Copenhagen’s An Open World: Bohr Conference 2013.We of transformation. HAU – Journal of Ethnographic thank organizer Ole Wæver and discussants during this Theory 3(3): 33–61. event. The SensibleDTU project, initiating the Copenhagen Kovanen L, Karsai M, Kaski K, et al. (2011) Temporal Social Networks Study, was made possible by a Young motifs in time-dependent networks. Available at: http:// Investigator Grant from the Villum Foundation (awarded arxiv.org/abs/1107.5646 (accessed 7 July 2014). to Sune Lehmann). Scaling the project up to 1000 individuals Latour B, Jensen P, Venturini T, et al. (2012) ‘The whole is in 2013 was made possible by an interdisciplinary UCPH always smaller than its parts’ – A digital test of Gabriel 2016 grant, Social Fabric (PI David Dreyer Lassen). Tarde’s monads. British Journal of Sociology 63(4): We thank everyone partaking in the SensibleDTU and 590–615. Social Fabric projects for valuable input to this text and the Lazer D, Pentland A, Adamic L, et al. (2009) Computational research underlying it, including research group leaders: social science. Science 323: 721–723. Assoc. Prof. Sune Lehmann (DTU Compute), Prof. David Pinch T (2011) Karen Barad, quantum mechanics, and the Dreyer Lassen (Economics), Assist. Prof. Jesper Dammeyer paradox of mutual exclusivity. Social Studies of Science (Psychology), Assoc. Prof. Joachim Mathiesen (Physics), 41(3): 431–441. Assist. Prof. Julie Zahle (Philosophy), and Assoc. Prof. Riles A (1998) Infinity within the brackets. American Rikke Lund (Public Health). Finally, we thank two anonym- Ethnologist 25(3): 378–398. ous reviewers for apposite comments helping us strengthen Ruppert E, Law J and Savage M (2013) Reassembling social our argument on complementarity vis-a` -vis the mixed meth- science methods: The challenge of digital devices. Theory, ods literature. Culture & Society 30(4): 22–46. Savage M and Burrows R (2007) The coming crisis of empir- References ical sociology. Sociology 41(5): 885–899. Anderson C (2008) The end of theory: The data deluge makes Stoller P (2013) Big data, thick description and political expe- the scientific method obsolete. Wired Magazine, 16.07, 23 diency. Huffington Post, posted 16 June 2013. Available at: June. www.huffingtonpost.com/paul-stoller/big-data-thick- Barad K (2011) Erasers and erasures: Pinch’s unfortunate descrption_b_3450623.html (accessed 7 July 2014). ‘uncertainty principle’. Social Studies of Science 41(3): Stopczynski A, Sekara V, Sapiezynski P, et al. (2014) 443–454. Measuring large-scale social networks with high reso- Boellstorff T (2013) Making big data, in theory. First Monday lution. PLoS ONE 9(4): e95978. 18(10). Available at: http://firstmonday.org/ojs/index.php/ Wang T (2013) Big data needs thick data. fm/article/view/4869/3750 (accessed 31 July 2014). Ethnography Matters, posted 13 May 2013. Available at: Bohr N (1957[1938]) Atomfysik og menneskelig erkendelse. http://ethnographymatters.net/2013/05/13/big-data-needs- Copenhagen: Schultz Forlag. thick-data/ (accessed 7 July 2014). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Big Data & Society SAGE

Complementary social science? Quali-quantitative experiments in a Big Data world:

Blok, Anders; Pedersen, Morten Axel
Big Data & Society , Volume 1 (2): 1 – Aug 6, 2014
Download PDF
6 pages

Loading next page...
 
/lp/sage/complementary-social-science-quali-quantitative-experiments-in-a-big-kZiX0xL9v2
Publisher
SAGE
Copyright
Copyright © 2022 by SAGE Publications Ltd, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses.
ISSN
2053-9517
eISSN
2053-9517
DOI
10.1177/2053951714543908
Publisher site
See Article on Publisher Site

Abstract

The rise of Big Data in the social realm poses significant questions at the intersection of science, technology, and society, including in terms of how new large-scale social databases are currently changing the methods, epistemologies, and politics of social science. In this commentary, we address such epochal (‘‘large-scale’’) questions by way of a (situated) experiment: at the Danish Technical University in Copenhagen, an interdisciplinary group of computer scientists, physi- cists, economists, sociologists, and anthropologists (including the authors) is setting up a large-scale data infrastructure, meant to continually record the digital traces of social relations among an entire freshman class of students (N> 1000). At the same time, fieldwork is carried out on friendship (and other) relations amongst the same group of students. On this basis, the question we pose is the following: what kind of knowledge is obtained on this social micro-cosmos via the Big (computational, quantitative) and Small (embodied, qualitative) Data, respectively? How do the two relate? Invoking Bohr’s principle of complementarity as analogy, we hypothesize that social relations, as objects of knowledge, depend crucially on the type of measurement device deployed. At the same time, however, we also expect new interferences and polyphonies to arise at the intersection of Big and Small Data, provided that these are, so to speak, mixed with care. These questions, we stress, are important not only for the future of social science methods but also for the type of societal (self-)knowledge that may be expected from new large-scale social databases. Keywords Principle of complementarity, method devices, quali-quantitative methods, social science experiments, computational social science, Big Data critique Introduction of complementarity’’ be brought to bear on current interdisciplinary research fields in the social sciences, The information regarding the behaviour of one and the which comprise both quantitative and qualitative meth- same object under mutually exclusive experimental set- ods and data-sets? Indeed, may Bohr’s far-reaching tings may .. . , in an often-used expression within atomic thinking inspire us to conjure a new epistemology as physics, suitably be characterized as complementary, in well as a new politics of ‘‘quali-quantitative’’ (Latour that – although their description in everyday language et al., 2012) methods for the social sciences? cannot be subsumed into one whole – they nevertheless each express equally important aspects of the total sum Department of Sociology, University of Copenhagen, Copenhagen, of thinkable experiences regarding the object. (Bohr, Denmark 1957 [1938]: 38; authors’ translation) Department of Anthropology, University of Copenhagen, Copenhagen, Denmark Such were the words of the renowned physicist Niels Corresponding author: Bohr (1885–1962) in a talk he delivered in 1938 to an Anders Blok, Department of Sociology, Øster Farimagsgade 5, audience of Danish anthropologists. We cite this pas- Copenhagen K, 1014 Denmark. sage because we want to ask: how can Bohr’s ‘‘principle Email: abl@soc.ku.dk Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http:// www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (http://www.uk.sagepub.com/aboutus/open- access.htm). 2 Big Data & Society It is important to note that, while Bohr was and still interferences may be expected to arise at the intersec- today is hailed for his contribution to modern physics, tion of Big and Small Data, provided that these are, so ‘‘[t]he full radical nature of Bohr’s views has not always to speak, mixed with care. been recognized’’ by physicists (Pinch, 2011: 6–7). Below, we explore such questions by way of discuss- Indeed, within both the natural and the social sciences, ing a specific social science experiment, which attempts there is widespread skepticism if not downright hostility to mix computational and ethnographic methods towards bringing together radically different research into what might be dubbed ‘‘Complementary Social methods and traditions in the hope of generating new Science,’’ namely the Copenhagen Social Networks and better insights into complex phenomena. The cross- Study. In this research project, an interdisciplinary disciplinary field of ‘‘computational social science’’ is a group of computer scientists, physicists, economists, case in point. Consider the following two citations, sociologists and anthropologists (including our- taken from the agenda-setting 2009 Science paper co- selves) has recently embarked on a novel attempt to authored by leading researchers from this nascent field, combine quantitative width and qualitative depth in and from a recent column in Huffington Post by US mapping the spatio-temporal details of a concrete anthropologist Paul Stoller (2013): social network. In short, a computational social science is emerging Social fabric: Remixing mixed methods that leverages the capacity to collect and analyze data with an unprecedented breadth and depth and scale. The Copenhagen Social Networks Study is a large-scale (Lazer et al., 2009: 722) cross-disciplinary research program jointly hosted by the Faculty of Social Sciences, University of Copenhagen, The problem of Big Data is here to stay, which means and the Department of Informatics and Mathematical that in the coming months and years we’ll need a legion Modelling at the Danish Technical University (DTU). of ethnographically trained analysts to produce ‘Thick Our 25-plus member big team collaborates to make con- Data’ – to save us from ourselves. (Stoller, 2013) tinuous recordings of social interactions at all commu- nication channels among an entire freshman class Lazer et al. (2009), of course, echo broader visions (N> 1000) of DTU students, using smart phones distrib- (in)famously made on behalf of the scientific ‘‘Big uted to students (as well as to members of the project Data revolution’’ such as Chris Anderson’s controver- group) as measurement devices (‘‘socio-meters’’). This sial claim about a supposed ‘‘end of theory’’ (2008). On allows us to digitally map out the ‘‘complete’’ social net- his part, Stoller draws heavily on a blog posting by work of an entire freshman class, including face-to-face Tricia Wang (2013), who argues that ‘‘ethnographers encounters via Bluetooth, geo-location proximities via must engage with Big Data,’’ for fear of being ‘‘mini- GPS, social network data via apps, and telecommunica- mized as a small line item on a budget, and relegated to tion data via call logs. The set-up also includes, by way the small data corner.’’ of ‘‘embedding’’ an anthropologist within the freshmen Our ambition in this piece is to sketch an alternative group for an entire year, ‘‘thick’’ ethnographic fieldwork to these two mutually exclusive (and mutually hostile) data on friendship and other social relations amongst the understandings of the role and value of (seemingly) same group of students. Simultaneously, researchers ‘‘big’’ quantitative data and (seemingly) ‘‘small’’ quali- track different components of the social fabric via the tative data methods and approaches in contemporary application of established survey methods (for an over- research and society. To do so, we pose the following view, see Stopczynski et al., 2014). This vast and hetero- questions: how do the kinds of knowledge that can be geneous collection of data on a large-scale social obtained from ‘‘Big’’ (computational) and ‘‘Small’’ network infrastructure is being used to study: (ethnographic) data, respectively, relate or not relate? Indeed, beyond the suggestion of Stoller and ‘‘like- (i) How information and influence are transmitted minded’’ social scientists voicing tout court concern and transformed in the DTU ‘‘social fabric’’ about Big Data, might the notion of thick data (ii) How friendships, networks and behaviors form, (c.f. op cit; see also Boellstorff, 2013) be put to better offline and online use to denote what relates, rather than what separates, (iii) How the researchers themselves study ‘‘Big Data’’ and handle issues of ethics and privacy innovative approaches to future social science experi- ments? Invoking Bohr’s principle of complementarity, we contend that social relations, as objects of both sci- Within this framework, a team of sociologists and entific and everyday knowledge, indeed depend on the anthropologists (senior scholars, doctoral students, and type of measurement device deployed for their observa- undergraduates) from Copenhagen University, led by tion. At the same time, we propose that new productive the two of us, conduct a joint ‘‘Ant-Soc’’ work package, Blok and Pedersen 3 which aims to push current boundaries for mixing and (i.e. ethnographic) data approaches in light of our cross-fertilizing quantitatively and qualitatively based experiences from the Copenhagen Social Networks social network research. We do this by exploring a Study project so far? To begin with, the collaborative number of closely interrelated research questions, research vision we advocate here differs from the hostile themes and methods at the core of current concerns stance towards Big Data by increasing numbers of in sociology, anthropology, and science and technology qualitative social scientists. Valuable as the emerging studies (STS) – as well as in the cross-disciplinary fields Critical Big Data Studies Paradigm (as one might call of computational, digital, and experimental social sci- it) undoubtedly will prove to be, it tacitly relies upon ence – including: and thus reproduces a problematic bifurcation between ‘‘hard’’ quantitative evidence in need of further inter- . Ethnographic fieldwork – how can ethnographic stu- pretation and ‘‘soft’’ qualitative data imbued with ‘‘the dies of friendship and other social network relations meaning’’ needed to close this hermeneutic gap. amongst students enrich or challenge computational As Boyd and Crawford (2012: 670) write, echoing approaches – and vice versa? Stoller’s and Wang’s positions: . Quali-quantitative methods – does the rise of com- putational social science lead to a reconfiguration of [I]t is increasingly important to recognize the value of traditional splits between quantitative and qualita- ‘small data’. [...] Take, for example, the work of Veinot tive research methods? (2007), who followed one worker – a vault inspector at . Big data experiments – what kinds of anthropo- a hydroelectric utility company – in order to under- logical and sociological experiments does our set- stand the information practices of a blue-collar up enable, and how might such innovations enrich worker. [.. .] Her work tells a story that could not be existing social-scientific designs? discovered by farming millions of Facebook or Twitter . Social life of Big Data – what new ethical, political accounts. and organizational challenges and opportunities does the rise of large-scale social databases pose to While we very much sympathize with Boyd and the social sciences and society at large? Crawford’s ambition to affirm the lasting value of eth- . Research collaboration – taken as an object of STS, nography and its critical potential, arguments such as what may be learned about cross-disciplinary collab- the above also tend to reproduce prevailing assump- oration from the research program itself? tions that ‘‘big’’ and ‘‘small’’ data and methods are simply mutually exclusive. This runs counter to what In terms of social science methods, the unique cross- we would like to think the Copenhagen Social disciplinary and cross-institutional set-up of the Networks Study project allows for, which is to make Copenhagen Social Networks Study allows for drawing ‘‘big’’ and ‘‘small’’ data mutually dependent and enhan- together two hitherto distinct literatures, on computa- cing, and thus potentially recalibrate this and other tional social science (e.g. Lazer et al., 2009) and mixed unhelpful bifurcations between so-called quantitative methods (e.g. Creswell, 2011), respectively. To the best and so-called qualitative data worlds. That, after all, of our knowledge, researchers in computational social is what complementarity in Bohr’s definition was all science are yet to acknowledge the potential significance about: an epistemological and ontological predicament, to their work of grounded participant-observation in whereby two phenomena or processes are at one and the ethnographic tradition. Conversely, the mixed the same time totally disparate and totally interdepend- methods literature so far has not picked up the chal- ent. As Karen Barad (2011: 444) puts the point: lenges and opportunities of large-scale digital data, and ‘‘Complementarity entails two important features: is therefore at risk of remaining trapped within an mutual exclusivity and mutual necessity. For two vari- obsolete distinction between qualitative and quantita- ables to be complementary they have to be both simul- tive (i.e. survey-based) approaches. This methodo- taneously necessary and mutually exclusive. Otherwise, logical remixing – which allows for new ways of what is the paradox?’’. Critical Big Data studies, it stitching together computational and ethnographic seems to us, risk losing the paradox of complementarity data – is what we propose to dub ‘‘complementary rather than benefitting from it. social science.’’ Another problem with such an emerging ‘‘critical consensus’’ amongst qualitative social scientists is its overly narrow understanding of what critique is. Bohr revisited: A complementary Put bluntly, there is something unsatisfactory social science? about reducing the intervention that sociologists and What can we say about the relationship between ‘‘big’’ anthropologists like us can make on Big Data realities or ‘‘deep’’ (i.e. computational) and ‘‘small’’ or ‘‘thick’’ and discourses to a one-dimensional question of 4 Big Data & Society deconstruction and debunking. Again, we are of course reconfiguring the very epistemological, methodological, not claiming that allegedly ‘‘neutral’’ incipient hege- and political playing field onto which the recently monic discourses on, e.g., so-called non-theoretical much-hyped discourses of ‘‘Big Data’’ social science computational social science can and should not be make claim. As an attempt to work around and thus questioned. All we are suggesting is that too much distort what is on the inside and what is on the outside default Big Data bashing runs the risk of tacitly relying of various conventionalized bifurcations and contrasts on an assumed vantage point from outside these dis- (researcher vs. research object; natural science vs. social courses and practices – a quite conservative observa- science/humanities; ‘‘hard’’ quantitative vs. ‘‘soft’’ tional ‘‘fly on the wall’’ position from which the qualitative data and approaches), our vision of a com- anthropologist and sociologist are taken to enjoy plementary social science contains the promise of an unique access to both her object of study and a broader ‘‘immanent,’’ non-skeptical critique (cf. Holbraad ‘‘context’’ of which it is supposedly a ‘‘part’’ (cf. Riles, et al., 2013) of narrowly technical and neo-positivist 1998). Take, for instance, Mike Savage and Roger celebrations of Big Data in research as well as non- Burrows’s (2007) otherwise pertinent points about research contexts. what they (following Nigel Thrift) dub ‘‘knowing cap- italism’’; surely, we might counter, there is more to the Quali-quantitative methods Big Data challenge than merely diagnosing a new form of capitalism? In a recent article aptly entitled ‘‘The whole is always In the Copenhagen Social Networks Study project, smaller than its parts,’’ Latour et al. ask: adopting such a conservative notion of critique would entail that physicists like Sune Lehman (the PI from [...] [I]s there a way to define what is a longer lasting DTU) were located fully within the large-scale social social order without making the assumption that there network database investigated by our interdisciplinary exist two levels [of individuals and structures]. [...] team, whereas sociologists and anthropologists, such as Instead of having to choose and thus to jump from the two of us, would be positioned on the outside of this individuals to wholes, from micro to macro, [we want Big Data reality and discourse. All we would do (or to] occupy all sorts of other positions, constantly rear- pretend to be doing) as so-called critical anthropolo- ranging the way profiles are interconnected and over- gists and sociologists, then, would be to observe pur- lapping. (2012: 591) portedly ‘‘native’’ computational social scientists by looking at them from the outside in, seeking to identify, To a large extent, this passage captures what the two of monitor, trace, describe and ultimately decode the more us hope to achieve from our quest to laterally assemble or less exotic ideas and practices of this scientific into a single research reality (or ‘‘research ‘‘tribe.’’ Yet, exciting and self-satisfying as this would relationality’’) the two hitherto bifurcated social scien- undoubtedly be, we would thereby end up replicating tific arenas of Big and Small data: by stubbornly resist- the dubious assumptions about mutual exclusivity ing the temptation to perceive the cross-disciplinary between quantitative Big Data and qualitative small Copenhagen Social Networks Study as comprised by data social science that we set out to transcend in the two mutually exclusive methodological and first place. epistemological domains – a quantitative and a Our vision of a complementary social science is qualitative one, respectively – we wish to insist on the meant to offer a way of avoiding such pitfalls. By potential for new and progressive forms of what Latour attempting to mix and even merge our own research et al. (2012) dub ‘‘quali-quantitative methods.’’ This, agendas, perspectives, and methodologies with our however, raises a new order of urgent questions. ‘‘native’’ computational social scientific ‘‘informants,’’ It is becoming increasingly clear that, within algo- we have from the onset of the project attempted to rithmically generated Big Data worlds such as the position ourselves within the large-scale social network digital database generated in our research program, database under investigation, not on the outside of it – the ‘‘part’’ is indeed often bigger than the ‘‘whole,’’ as as would be the aspiration of many conventional Latour et al. suggest. This is illustrated, for instance, by science studies approaches. For only by in this way the way in which complexity in quantitative social net- strategically striving to partly collapse our own work mappings tends to increase, as opposed research interests with those of the other researchers to decrease, the moment one looks not for ‘‘aggregate’’ involved in the Copenhagen Social Networks Study static structures but for the replication, say, of ever can we hope to move from mutual exclusivity to inter- more fine-grained ‘‘temporal motifs’’ in dynamic inter- dependency in the complementarity between computa- action patterns across smaller groups (Kovanen et al., tional and ethnographic approaches. We like to think 2011). But are such granularities necessarily the same in of this as a germane strategy for gradually Big and Small data worlds? Blok and Pedersen 5 By investing so heavily in the promises of new large- can we quantify the importance of ‘‘ambience,’’ ‘‘atmos- scale ‘‘digital trace’’ databases, Latour et al. may risk phere,’’ ‘‘togetherness,’’ and so on in this respect – losing sight of the ways in which disparate data worlds ‘‘quali-quantities’’ which, from a standard sociological rub off against and emerge from each other, rather than and anthropological point of view, would be purely producing new seamless ‘‘wholes.’’ Within computa- qualitative? What, in turn, might such new data assem- tional social science, for instance, the focus on granu- blages that ‘‘stitch together’’ data worlds produced larity ‘‘drives forward a concern with the microscopic, through computational and ethnographic methods do the way that amalgamations of databases can allow to established concepts of ‘‘personhood,’’ ‘‘sociality,’’ ever more granular, unique, specification’’ and ‘‘politics’’? (Ruppert et al., 2013: 38). Accordingly, in the Copenhagen Social Networks Study project, we hope Conclusion to extend the method of quali-quantitative research by making the very study of social relations – by means of We are well aware that our vision of a complementary ethnographic fieldwork, surveys, digital traces, and so social science for the 21st century raises many new on – part and parcel of the experimental set-up itself. questions and numerous potential objections. Apart By doing so, we put ourselves in a position to experi- from the sheer ‘‘technical’’ challenges of remixing dif- ment on social science complementarity in practice: ferent methods, devices, infrastructures, and data forms how, we ask, may our experimental settings of obser- (ranging from ethnographic field notes to database vation themselves be experimentally varied so as to algorithms) into ‘‘thick’’ data, questions arise at the create different kinds of mutual necessity between Big heart of the philosophy of science. For example: if it (computational) and Small (ethnographic) data? What is indeed the case that, in computational social science, forms of ‘‘granularity,’’ ‘‘thickness,’’ and ‘‘depth’’ arise we leave behind the search for causal statistical model- from engaging in, rather than simply tracing, concrete ing and enter a new world of visual ‘‘pattern recogni- work of computational data design, compilation and tion’’ (Ruppert et al., 2013: 36), then what happens to assemblage, alongside ethnographic descriptions (see time-honored distinctions between numbers and narra- also Kockelman, 2013)? tives; description and explanation; and indeed, simula- In pursuing this kind of experimental approach, new tion and the real world? Such questions, it seems to us, and difficult questions of research ethics emerge, in part are not just becoming ever more pertinent with ongoing because established conventions on how to deal respon- digital social database developments (including recent sibly with issues of privacy, confidentiality, etc., differ scares and scandals). They can also only be addressed widely between (and to some degree also within) com- in the same ‘‘messy’’ interface between basic research, putational and ethnographic approaches. Here as well, social and political engagement, and collaborative however, much-needed dialogues and imports should experimentation, which Niels Bohr pioneered in go both ways, as computational researchers stand to his time. learn from routine experiences, on the part of field- Furthermore, as noted, our vision of a complemen- workers, of gaining access to ‘‘private’’ layers of peo- tary social science must respond to legitimate and ple’s lives far beyond what should ever be conveyed in urgent ethical and political concerns – raised by both ‘‘public’’ research. We need to ask: what could ethically critics and supporters of computational data – with appropriate, and indeed ethically desirable, forms of regard to issues of surveillance, privacy, and future complementary social science data be? misuse of data. Once again, without claiming to have At this relatively early stage of our collective endea- in any way ‘‘solved’’ these issues, the Copenhagen vors, it is still premature to give definite answers to such Social Networks Study team has already taken some epistemological and ethical questions. What we can important steps in trying to make data available to provide, however, is a flavor of what we have in the research subjects themselves via apps and websites mind. For example: how might the combination of (including the official project website: https:// ethnographic embedding and computational data help www.sensible.dtu.dk/), as well as more interactive on- us understand the way various affective moods, and offline forums, and by including members of the impulses, and rumors spread throughout a collectivity? project team themselves in the experiment. In this sense, As Ruppert et al. (2013: 35) suggest, the transactional our notion of complementarity extends to the mutual data favored by most computational social science dependencies of researchers and their subjects; to the lends itself to entirely ‘‘non-individualist’’ accounts of greatest extent possible, research subjects should be cast social life, where the play of fluid and dynamic trans- as co-producers of knowledge about themselves, just as actions is the focus of attention. In our setting, one researchers should strive whenever feasible to render question might be: what role does parties and partying themselves subject to their own research questions play in the formation of social connections, and how and methods. Our collaborative research program, 6 Big Data & Society Boyd D and Crawford K (2012) Critical questions for big then, is not just about methods and results; it is also, data. Information, Communication & Society 15(5): more broadly or ‘‘thickly,’’ an experiment in ‘‘data 662–679. democracy.’’ Creswell JW (2011) Controversies in mixed methods research. In: Denzin NK and Lincoln YS (eds) The SAGE Declaration of conflicting interests Handbook of Qualitative Research. 4th ed. London: The author declares that there is no conflict of interest. SAGE Publications, pp. 269–283. Holbraad M, Pedersen MA and Viveiros de Castro E (2013) Funding The politics of ontology: Anthropological positions. (Theorizing the contemporary). Cultural Anthropology. This research received no specific grant from any funding Available at: www.culanth.org/fieldsights/462-the- agency in the public, commercial, or not-for-profit sectors. politics-of-ontology-anthropological-positions (accessed 7 July 2014). Acknowledgements Kockelman P (2013) The anthropology of an equation. A first version of this text was presented at the University of Sieves, spam filters, agentive algorithms, and ontologies Copenhagen’s An Open World: Bohr Conference 2013.We of transformation. HAU – Journal of Ethnographic thank organizer Ole Wæver and discussants during this Theory 3(3): 33–61. event. The SensibleDTU project, initiating the Copenhagen Kovanen L, Karsai M, Kaski K, et al. (2011) Temporal Social Networks Study, was made possible by a Young motifs in time-dependent networks. Available at: http:// Investigator Grant from the Villum Foundation (awarded arxiv.org/abs/1107.5646 (accessed 7 July 2014). to Sune Lehmann). Scaling the project up to 1000 individuals Latour B, Jensen P, Venturini T, et al. (2012) ‘The whole is in 2013 was made possible by an interdisciplinary UCPH always smaller than its parts’ – A digital test of Gabriel 2016 grant, Social Fabric (PI David Dreyer Lassen). Tarde’s monads. British Journal of Sociology 63(4): We thank everyone partaking in the SensibleDTU and 590–615. Social Fabric projects for valuable input to this text and the Lazer D, Pentland A, Adamic L, et al. (2009) Computational research underlying it, including research group leaders: social science. Science 323: 721–723. Assoc. Prof. Sune Lehmann (DTU Compute), Prof. David Pinch T (2011) Karen Barad, quantum mechanics, and the Dreyer Lassen (Economics), Assist. Prof. Jesper Dammeyer paradox of mutual exclusivity. Social Studies of Science (Psychology), Assoc. Prof. Joachim Mathiesen (Physics), 41(3): 431–441. Assist. Prof. Julie Zahle (Philosophy), and Assoc. Prof. Riles A (1998) Infinity within the brackets. American Rikke Lund (Public Health). Finally, we thank two anonym- Ethnologist 25(3): 378–398. ous reviewers for apposite comments helping us strengthen Ruppert E, Law J and Savage M (2013) Reassembling social our argument on complementarity vis-a` -vis the mixed meth- science methods: The challenge of digital devices. Theory, ods literature. Culture & Society 30(4): 22–46. Savage M and Burrows R (2007) The coming crisis of empir- References ical sociology. Sociology 41(5): 885–899. Anderson C (2008) The end of theory: The data deluge makes Stoller P (2013) Big data, thick description and political expe- the scientific method obsolete. Wired Magazine, 16.07, 23 diency. Huffington Post, posted 16 June 2013. Available at: June. www.huffingtonpost.com/paul-stoller/big-data-thick- Barad K (2011) Erasers and erasures: Pinch’s unfortunate descrption_b_3450623.html (accessed 7 July 2014). ‘uncertainty principle’. Social Studies of Science 41(3): Stopczynski A, Sekara V, Sapiezynski P, et al. (2014) 443–454. Measuring large-scale social networks with high reso- Boellstorff T (2013) Making big data, in theory. First Monday lution. PLoS ONE 9(4): e95978. 18(10). Available at: http://firstmonday.org/ojs/index.php/ Wang T (2013) Big data needs thick data. fm/article/view/4869/3750 (accessed 31 July 2014). Ethnography Matters, posted 13 May 2013. Available at: Bohr N (1957[1938]) Atomfysik og menneskelig erkendelse. http://ethnographymatters.net/2013/05/13/big-data-needs- Copenhagen: Schultz Forlag. thick-data/ (accessed 7 July 2014).

Journal

Big Data & Society – SAGE

Published: Aug 6, 2014

Keywords: Principle of complementarity; method devices; quali-quantitative methods; social science experiments; computational social science; Big Data critique

References

  • Erasers and erasures: Pinch’s unfortunate ‘uncertainty principle’
  • Critical questions for big data
  • Controversies in mixed methods research
  • The anthropology of an equation. Sieves, spam filters, agentive algorithms, and ontologies of transformation
  • ‘The whole is always smaller than its parts’ – A digital test of Gabriel Tarde’s monads
  • Computational social science
  • Karen Barad, quantum mechanics, and the paradox of mutual exclusivity
  • Infinity within the brackets
  • Reassembling social science methods: The challenge of digital devices
  • The coming crisis of empirical sociology
  • Measuring large-scale social networks with high resolution