10.6092/issn.2038-3460/17431
The “imperative of data” seems to be at the core of current debates concerning relationships between technology and society, as well as the status of knowledge, freedom and identity. Departing from the plenary roundtable Datafication and Technoscience from Below: Sharing, Moving, Opening at the 7th STS Italia Conference held in Padua (June 2018), the authors go through different aspects of the sociotechnical process of datafication, emphasizing from complementary STS perspectives some of the most urgent and emergent challenges posed by the constant exploitation of data at an economic and financial level. Topics covered are ambivalences of datafication and their consequences for everyday life (Pellegrino); the epistemological consequences of the “race to the bottom-up” and fake news as ultimate drift of such a race (Söderberg); data-logies and grassroots epistemologies as conditions for possibility for a datafication from below (Milan).
application/pdf
2038-3460
17431
1
en
Stefania Milan
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
infrastructuring
Datafication from Below: Epistemology, Ambivalences, Challenges
Text.Serial.Journal
https://tecnoscienza.unibo.it/
10
Crossing Boundaries
2019-07-16
2023-06-26
2019-07-16
2023-10-05
89-113
TS
Stefania Milan
2019/07/16
10.6092/issn.2038-3460/17431
89
University of Amsterdam
2038-3460
1
infrastructuring
en
113
Datafication from Below: Epistemology, Ambivalences, Challenges
https://tecnoscienza.unibo.it/article/download/17431/16281
10
                                                                            Crossing Boundaries
                                                                                                      89




Datafication from Below: Epistemology,
Ambivalences, Challenges

Giuseppina Pellegrino Johan Söderberg                                       Stefania Milan
        University of Calabria                University of Gothenburg      University of Amsterdam




  Abstract: The “imperative of data” seems to be at the core of current de-
  bates concerning relationships between technology and society, as well as
  the status of knowledge, freedom and identity. Departing from the plenary
  roundtable Datafication and Technoscience from Below: Sharing, Moving, Opening
  at the 7th STS Italia Conference held in Padua (June 2018), the authors go
  through different aspects of the sociotechnical process of datafication, em-
  phasizing from complementary STS perspectives some of the most urgent
  and emergent challenges posed by the constant exploitation of data at an
  economic and financial level. Topics covered are ambivalences of datafication
  and their consequences for everyday life (Pellegrino); the epistemological
  consequences of the “race to the bottom-up” and fake news as ultimate drift
  of such a race (Söderberg); data-logies and grassroots epistemologies as
  conditions for possibility for a datafication from below (Milan).

  Keywords: datafication; post                      truth;   fake   news;     grassroots     data
  epistemology; infrastructuring.

  Corresponding author: Giuseppina Pellegrino, Dipartimento di Scienze
  politiche e sociali, Università della Calabria, Via Pietro Bucci – 87036
  Arcavacata di Rende (CS). E-mail: giuseppina.pellegrino@unical.it




Inside “the Below”: Ambivalences of Datafication and
Infrastructuring of Everyday Life
Giuseppina Pellegrino

   Contemporary social and daily life is increasingly subjected to a grow-
ing and apparently overwhelming phenomenon, that of retrieving, pro-
ducing, accumulating, tracking and transforming into (economic and fi-
nancial) value enormous amounts of data through specific technoscien-

TECNOSCIENZA
Italian Journal of Science and Technology Studies
10 (1) pp. 89–113 – ISSN 2038–3460
                                                         2019
www.tecnoscienza.net
90                                                          Tecnoscienza – 10 (1)

tific infrastructures (also called “Big Data Analytics”, see Mosco 2017).
All this summarized as datafication – a complex and contradictory soci-
otechnical process – questions the boundaries, possibilities and condi-
tions of knowledge, freedom, and identity. It is of interest that such a
process, as typical in technology and media history, is subjected itself to a
constant dichotomic and oppositive binary thinking (Sturken and Thom-
as 2014). On the one hand, the apocalyptic register of those framing
technology (in this case, data) as coming exclusively “from above”, tools
to exert and impose power, namely the power of surveillance and the end
of privacy as we used to conceive it. On the other hand, the more “inte-
grated” approach viewing datafication as the trigger and the field of new
opportunities, benefits and progressive futures “from below” (on the rhe-
torical figure “above vs below”, see Söderberg in this issue).
     This contribution frames the issue of datafication and that of “the be-
low” through a long-term perspective, looking at their founding and un-
derlying processes, namely the broader and structural condition of “be-
coming digital”, to quote Vincent Mosco’s book title (Mosco 2017). To
put it differently, datafication could not exist without – and stays only
within – the pervasive digitization of information, whose grounds were
built up in the early age of Cybernetics and Informatics, under the flags
of freedom and liberty (Kelty 2014). Such an “installed base” (Star and
Ruhleder 1996) of data(fication) infrastructure carries out and brings
about the distinctive characters of datafication, revealing at the same time
its deep ambivalences towards the way organizations, groups and individ-
uals act through dispersed sociotechnical networks where the “imperative
of data” is at the core. Just to quote a few of them, data-intensive science,
precision medicine, machine learning and artificial agents, open data in
Public Administration infrastructures, and the emergence of a “quanti-
fied self” represent the high diversity of the forms datafication can take.
     As any new technology or sociotechnical process (Marvin 1988; Stur-
ken and Thomas 2004), datafication shows the persistence and durability
of hopes and horrors as drivers of the public discourse on technology, a
discourse regimen often inspired by presentification, obsolescence and
revolutionary perspectives (Pellegrino 2015).
     Therefore, while mainstream media concentrate on and fuel scandals
concerning the treatment and security of data and information on social
media, focusing on the risks of manipulation and cheating (e.g. the Face-
book/Cambridge Analytica Datagate; Mueller’s investigation on Rus-
siagate), technoscientific infrastructures of data and datafication can and
do act from below, in a myriad of sites, fields and circumstances (on “da-
tafication from below”, see Milan in this issue). Whereas the media dis-
course contributes to shape the public and rhetorical side of datafication,
putting emphasis either on its dark and dystopic or on its bright and
utopic dimension, practices from below, grounded in social movements
as well as in everyday routines, are the field where concrete consequences,
linkages and nuances in between the dark and the bright take form and
Pellegrino, Söderberg & Milan                                                       91
are brought to life. This contribution departs from digitization as the
foreground and installed base of datafication, distinguishing the two pro-
cesses and also comparing them. Then, ambivalences of datafication will
be highlighted. Eventually, the concept of “infrastructuring of everyday
life” (Pellegrino 2018) will be proposed as a key to go “inside the below”
and look at the material as well as hidden texture through which datafica-
tion envelopes our lives.

Datafication and Digitization

    Data and information are not the same thing; the typical differentia-
tion of the two is based on the concepts of “processing” and “interpreta-
tion”, which precede data and make information possible. New episte-
mologies in Human and Social Sciences, as well as the postmodern turn,
showed how data itself is not an objective construct, rather fully con-
structed, to the extent that the same concept of “raw data” is an idealiza-
tion, if not an “oxymoron” (Gitelman 2013).
    The phenomenon of Big Data (a buzzword as well) is pervading the
debate in all of the sciences, claiming for a revolutionary approach and
endless possibilities of quantification, aggregation and analysis; whereas
quantification itself is not at all a new phenomenon, the “big” of big data
concerns unprecedented possibilities linked with putting data in relation
with others (data, individuals, groups, infrastructures, and so on). Actual-
ly,

    Big Data is notable not because of its size, but because of its relationality
    to other data. Due to efforts to mine and aggregate data, Big Data is fun-
    damentally networked. Its value comes from the patterns that can be de-
    rived by making connections between pieces of data, about an individual,
    about individuals in relation to others, about groups of people, or simply
    about the structure of information itself. (boyd and Crawford 2011, 1-2)

   In particular, with reference to spatial science, “two things that are
making data suddenly big are the datafication of the individual and the
geocoding of everything” (Cresswell 2014, 57). However, we could not
understand the width of datafication without referring to digitization. It is
crucial to recognize that it is through Information and Communication
Technologies, namely digital technologies, that this trend towards accu-
mulation and valorization of data has become more and more possible, as
well as powerful and effective.
   We could say that digitization is the general form which transformed
the possibility to cope with data and information, through a crucial re-
duction of complexity: bits (of information) are an abstract, homogene-
ous, discrete and numerical formatting, enhancing the retrieval, transfer,
cumulation of information through digital technologies and especially
digital computing. Digital is more of a quality than a quantity, a relation
92                                                           Tecnoscienza – 10 (1)

rather than a content (to use Palo Alto’s axioms). It refers to signals, in-
formation, data, images, contents, devices, as well as the media.
     Digital formats enable synthesis, comparison, storage and retrieval of
information. At the same time, it increases exponentially production and
makes selection more difficult (see Pellegrino 2018).
     If datafication is the latest version of obedience to the imperative of
quantification and purification which has so much marked the emergence
of the modern world and modern science (see Latour 1993a), digitization
is its precursor, as well as the “installed base” of data(fication) infrastruc-
ture (see Star and Ruhleder 1996). On the one hand, datafication goes far
beyond digitization: data are treated, manipulated and mined (sometimes
even “cooked”, Gitelman 2013) through more and more sophisticated
analytics techniques, not simply digitized. On the other hand, datafication
depends on digitization and even more on the increasing convergence
with advanced digital technologies, namely the Cloud and the Internet of
Things which constitute the sociotechnical infrastructure of what has
been named as “the Next Internet”, an increasingly integrated system that
is accelerating the decline of a democratic, decentralized and open-source
Internet (Mosco 2017, 5).
     From such a dependence it follows that datafication amplifies and en-
riches the multiple and diverse contradictions already present in digitized
sociotechnical infrastructures, adding to them peculiar ambivalences.

Ambivalences and Challenges of Datafication

    As other modern and contemporary phenomena, datafication shows
the significance of a classical category to analyze modern culture, that of
Simmelian ambivalence: a quality which connects as much as it separates
(individuals, groups, objects, and so on). In ambivalence, apparent and
jarring contradictions co-exist, on the one hand connecting and on the
other separating. In what follows some of these contradictions, consid-
ered as particularly challenging, are highlighted. They refer to trends
which are not specifically born with datafication, rather emphasized and
consolidated by it.

Exposure of the (Quantified) Self and (Co)Dataveillance

    Datafication infrastructure is deeply knotted to individuals’ bodies
and everyday life, due to the increasing level of quantification of human
body parameters, behaviour and ordinary practices. Mobility and porta-
bility of the Internet connection, the pervasiveness of social media along
with the expanding area of apps as proprietary platforms independent
from the “mainstream” web, all contribute to personalize and commodify
the act of quantification under the big promise to enhance life.
Pellegrino, Söderberg & Milan                                                        93
    However, just how much they enhance life is questionable, particularly in
    light of how constant, if not obsessive, attention to the quantities of life
    can diminish attention to its qualities or, even worse, reduce these qualities
    to a set of data points (Mosco 2017, 105).

    In this respect, especially health apps can be both an ambiguous, if
not controversial pedagogical tool and a new subtle medicalization engine
(Maturo et al. 2016). All this adds to the massive and deliberate self-
exposure strongly fuelled by digital media and social networks in particu-
lar, Facebook and Instagram in primis, which have marked the end of in-
timacy and privacy as pillars of the early modern bourgeois society. The
(mass) media and the myth of the mediated center (Couldry 2012), with
their emphasis on media celebrity, have been reinforced and amplified by
digital media and social networks. On the other hand, the commodifica-
tion of the individual as customer and consumer through the infrastruc-
tures of Big Data Analytics is also designed to advantage big corporations
owning much of those infrastructures, namely the Big 5 (Google, Ama-
zon, Facebook, Apple and Microsoft – see Mosco 2017). In fact,

    there is value in the things that are digitized and connected in the Internet
    of Things, but there is often more to be made from the data the devices
    generate, the valuable information that makes up the commodified self
    (Mosco 2017, 106).

     Value extracted from and attached to data – a big field named as Big
Data Analytics – is a real battlefield for business enterprises (Degli Espos-
ti 2014), but also increases dataveillance, a much older term than datafica-
tion, coined by Roger Clark in the ‘80s (Clark 1988). It refers to mass sur-
veillance through personal data systems used to monitor people’s behav-
ior.
     What is changed with reference to dataveillance nowadays, is the per-
vasiveness and literal embodiment of such personal data system, along
with the potential of horizontal and reciprocal surveillance based on data-
fication. In other words, as it happens with mobile communication, sur-
veillance becomes coveillance (Rainie and Wellman 2012), a peer-to-peer
phenomenon, more than a top-down process. The way we watch each
other goes back to pre-modern ways of life, when the group (clan or trib-
al) dimension was very strong. But symmetry of control and surveillance
is also linked to the concept of “below” and its epistemological conse-
quences when such a symmetry is fully pursued, as in Söderberg’s critical
review of the “race to the bottom”, and Milan’s “datafication from be-
low” overview (both in this issue).
94                                                             Tecnoscienza – 10 (1)

Pervasivity of Algorithms and Fragility of Machine Learning

    Notwithstanding the size is not the most distinctive characteristic of
Big Data, as already said, it is undoubted that the multiplication and in-
crease of the quantity of data and information – often labeled as “data
deluge” and “information overload” – represent one of the most evident
consequences of digitization, due to its power of homologation. Reducing
any piece of information to a binary digit (bit) means to make its
(re)production and use easier and easier. At the same time, what has
changed is the way such a massive amount is turned into (what is consid-
ered to be and validated as) knowledge, which of course is not the same
thing as data and information.

     The algorithmic assessment of information, then, represents a particular
     knowledge logic, one built on specific presumptions about what
     knowledge is and how one should identify its most relevant components.
     That we are now turning to algorithms to identify what we need to know is
     as momentous as having relied on credentialed experts, the scientific
     method, common sense, or the word of God (Gillespie 2014, 168).

    In other words, algorithms are the current compass to orient ourselves
in the contemporary ocean of data and information, and especially in the
Internet, becoming tools to define and build up public relevance.
    Despite their controversial status, algorithms are entitled with objec-
tivity and considered to be impartial, but as any (socio)technical piece of
infrastructure they are imbued with negotiations, assumptions and biases,
often purified in the public discourse which construct and re-affirms their
very relevance. Amongst all algorithms, those devoted to feed Machine
Learning predictive processes, show the fragility as well as all the contra-
dictions, “impurities” and heterogeneity of emerging AIs. As STS have
widely pointed out, there is nothing such as a neutral or pure technology.
Therefore, it is not at all surprising that the various and increasing at-
tempts to build intelligent agents manifest their limits, embedding and
reproducing the messy, chaotic and contingent processes of learning and
judging. The claim to improving human limits overcoming biases, preju-
dices and moral dilemmas is far from being reached.
    Instead, intensive datafication and machine learning amplify and ex-
acerbate those very limits, embedding them inside emerging sociotech-
nical infrastructures, as in the case of facial recognition technologies for
law enforcement (Vincent 2019).


Increasing Data Manipulation and Scarcity of (Data) Literacy

   The “race to the bottom-up” (Söderberg in this issue) and the call for
a generalized and extensive epistemology of symmetry have to confront
Pellegrino, Söderberg & Milan                                            95
the persistence and emergence of asymmetries about skills and literacy in
treating and making use of data in many contexts and practices.
    Possibilities to manipulate and create (fake) data through AI-based
technologies, as in the case of Deepfakes videos, pose unprecedented and
unexpected ethical challenges, blurring the boundaries between truth and
falsity, reality and fiction to extreme levels (Barber 2019).
    This type of manipulation and other type of fakes are often oriented
to achieve malicious goals, especially in the field of political consensus
and electoral propaganda (it is notable that in occasion of the latest Eu-
ropean elections Facebook removed over 200 fake accounts in the flood
of fake-based far-right propaganda – see Lapowski 2019).
    On the other hand, skills and literacy to face with data deluge, infor-
mation overload and algorithmic editorial processes are neither promoted
nor widespread at educational and societal level. An exception in this re-
gard is constituted by the case of Chinese government recently pushing
for special programs on Big Data and AI education at school
(http://www.chinadaily.com.cn/a/201903/14/WS5c89bda6a3106c65c34e
e983.html).
    However, it has to be noted that delegation to non-humans widely
pointed out by Latour and ANT is going to converge with the specific
“autonomy” inscribed into machines able to learn and to be trained (not
without biases, as annotated above) and aimed at generating new kinds of
expert systems and validated (algorithmic-based) knowledge.
    The ambivalence here is in the peculiar way new media technologies
enter and sometimes disrupt older and more recent skills, practices and
literacy.
    Therefore, while new (information and data) literacy is needed, “old-
er” types of literacy seem to be disappearing because of scarce exercise or
insufficient education and training. Rates of functional illiteracy in adult
population seem to be significant in many Western countries, and beyond
(ELINET 2015). The frame is completed by very pessimistic studies like
those carried out by the German neuroscientist Manfred Spitzer, which
theorize the emergence of a peculiar form of cognitive decay and break-
down due to ICT overuse, called “digital dementia” (Spitzer 2012).
    Such a perspective goes far beyond that of an anthropological trans-
formation, assuming digital natives and latest generations growing up dig-
ital are losing terrain and domain with reference to brain development
and evolution of cognitive and emotional skills.
    To sum up, datafication questions the feasibility and adequacy of lit-
eracy and education systems, as well as the ethics (to be) embedded in
tools, dispositive and infrastructures which mobilize new routines, new
ways of doing things in our daily life, as well as new forms of knowing,
judging and trusting our human and non-human companions.
96                                                          Tecnoscienza – 10 (1)

Inside “the Below”: Infrastructuring the Everyday Life

    Going inside “the below” (of datafication) means to recognize that the
very techniques which can result into fake news as a drift of the “below
imperative” and the principle of symmetry (Söderberg in this issue) can
also constitute an infrastructure to emancipate and mobilize marginal
groups. Various social and protest movements have built upon grassroots
data epistemologies (Milan in this issue), to the extent of configuring pe-
culiar forms of hybrid digital activism (Treré 2019).
    Beside the challenges exposed above, however, datafication and digit-
ization surround at both material and dematerialized level our daily rou-
tines, our practices and our sense making of what is real, true and trusta-
ble.
    Therefore, in order to disentangle ambivalences of datafication from
below, and what “below” itself implies and contains, this contribution
aims to propose a further STS insight to data and their consequences.
Such an insight consists of looking at everyday life as assembled through
and shaped by infrastructures from which data, information and
knowledge emerge as ecological relations with practices, contexts and
boundary objects (Star and Griesemer 1989). In particular, the concept of
everyday life as a continuous process of infrastructuring seems to be a
consistent tool to frame the ecologies and boundaries of appropriation,
care and maintenance of our digitized lives (see Pellegrino 2018). All of
us, and not only activists in grassroots movements, are called to act from
below, coping with and shaping the infrastructuring of our digitized eve-
ryday lives, a process which becomes routinized and black-boxed until
breakdowns and doubts make it emerge again and again.
    Being similar to ecological, fragile and highly diverse textures (Star
and Bowker 2006), day-by-day infrastructuring processes allow to put at
the center the relationship between visibility and invisibility, the role of
doubt and breakdown, the installed base of knowledge, routines and
common sense as well as the practices of care, maintenance and repair
where innovation and renewal can emerge (Jackson 2014).
    Coping with challenges of datafication means also acting from below à
la de Certeau, resisting through interstitial spaces, including those of vol-
untary on line dis-connection and media refusal (Kania-Ludholm 2018).
Deliberate interruption of digital ubiquitous communication can enact
different practices of care and repair, as well as alternative sense-making
of technoscientific infrastructures from below, where the below is our
day-by-day struggling with humans and non-humans.



                                    ***
Pellegrino, Söderberg & Milan                                               97

Post-truth: The Epistemological Race to the Bottom-
up

Johan Söderberg

    In this contribution, I will reflect over the rhetorical figure whereby a
“bottom” position is opposed to an “above” position, serving as a grid for
normative and theoretical orientation in STS interventions. The same bi-
nary opposition can alternatively be spoken of as an “outsider” against an
“insider”, or a “lay person” against an “expert”. The last couple of con-
cepts gives a hint about the direction and the relevance of such a figure
within the STS context. More examples of interest for the STS field are
patients vis-à-vis doctors, as well as users vis-à-vis designers. In spite of
the widespread prevalence of this rhetorical figure in the literature, it has
not yet been rendered explicit and made into the subject of a sustained
reflection. Typically, the binary opposition between the two – above and
bottom – is taken as a starting point of the empirical inquiry. We are sup-
posed to know intuitively what actor is on the bottom rung and what ac-
tor is on the top rung on the ladder. The lack of clarity about the criteria
on which this judgment draws, is a growing liability in the study of sci-
ence and technology. Every actor that has a message to sell to the public
will try to pass it off as coming from the bottom-position, thereby laying
claim to the legitimacy that has been invested in that position by society.
This is most clearly demonstrated in the phenomenon of astroturfing
(McNutt and Boland 2007).
    My contention is that the above mentioned rhetorical figure engen-
ders a ‘race-to-the-bottom’ that the predominant, theoretical and episte-
mological tenets in STS are ill suited to deal with, because those tenets
cannot register cases when politically and/or epistemological weak actors
are fronts for more powerful actors. New theories are needed that give
guidance to inquiries into what kind of bottom-positions are really at the
bottom and what bottom-positions are, on a closer inspection, much
higher up in the hierarchy, when factual statements are being made. Lack
of clarity in this regard is widespread in the literature, because it is rooted
in some widely shared, almost foundational, philosophical and epistemo-
logical tenets. It is the deconstruction of actors’ truth-claims, during the
past forty years, that has brought about a corresponding overinvestment
in the claims that actors are now making to be speaking from a margin.
The normative significance of deconstructing scientific truth claims rests
on the assumption that such assertions are welded by powerful actors in
order to extend their epistemological authority over less resourceful ac-
tors. Hence, the symmetrical treatment of truth and falsity is generally
taken to level the playing-field between more and less established actors
(Ashmore 1996).
98                                                          Tecnoscienza – 10 (1)

    One explanation for the widespread appeal of this approach could be
that it allows for a “have-the-cake-and-eat-it-strategy” within the scholarly
community. A symmetrically conducted case study carries a normative
load without requiring of the scholar to render his/her political views ex-
plicit. Hence, the scholar may honor the academic values of objectivity
without giving up on being critical. The drawback being, however, that
the position of the bottom acquires a foundational importance in the
symmetrical-yet-normative inquiry. The discursive construction is put out
of bounds of empirical scrutiny. If we, in accordance with the symmetry
principle, exclude the possibility that a propositional statement refers to a
corresponding state in the external world, by which we could otherwise
have told apart a better from a worse statement, then we need to assign
this discriminatory function to some other point of reference instead.
There is no way of navigating in a commonly lived world without having
some means of weighting conflicting, factual claims against each other.
That would be to confine oneself to a state of eremitic isolation. A classic
alternative to the correspondence theory of truth is to discriminate among
the different claims on the basis of their internal coherence (or lack
thereof). The known drawback is that this approach closes in upon itself,
providing no leverage to differentiate between statements in relation to
the external world.
    The rhetorical figure of appealing to the actors’ marginalized position
in a hierarchical order seemingly resolves this epistemological quagmire,
by shifting the debate from the epistemological level to a moral register
instead. Now, moving in the moral register, it is possible to assess the va-
lidity of statements about the world by referring to the relative marginali-
zation of the actors making those statements. That does not mean that
whatever a marginalized actor is uttering is to be taken as true, reasonable
and consistent. On the assumption that we have decided in advance to
treat all knowledge statements as equally (in)valid, this ought not to be of
any concern. It suffices to know that those utterances are not being given
the same credulity in society as other statements that are supported by
scientific institutions and expertise. This in itself justifies a preferential
treatment of the marginalized actor’s perspective over other perspectives.
Although the point of departure of this argument is an idea about fair-
ness, it can easily be aligned with one well-established notion of scientific
objectivity. This interpretation of ‘objectivity’ puts stress on bringing the
greatest number of different perspectives on a question. Hence, it is the
very marginality of a perspective that makes it so precious in the efforts to
tell the whole story and to give the full picture. In one stream of feminist
STS, standpoint epistemology, this is known as “hard objectivity”. It is
opposed to the skewed forms of objectivity that, although abiding to the
strictures of the scientific method, contributes to marginalizing women’s
perspectives in the sciences, hence rendering the sciences less objective
than they otherwise could have been (Harding 1995).
    This offers a compelling solution to the dilemma of how to discrimi-
Pellegrino, Söderberg & Milan                                              99
nate between conflicting statements about the world without making as-
sumptions about the truth content of those statements. It is so elegant a
solution, in fact, that it exercises a gravitational pull even on theoretical
positions in the STS community that are avowedly apolitical, such as in
the ANT-and-after-tradition. Although authors in the latter tradition re-
frain from declaring normative commitments, their selection of problems
for study such as, for instance, users (Woolgar 1990), patient groups
(Rabeharisoa and Callon 2004), and disabled (Blume et al. 2014), is sur-
prisingly consistent with the cases being studied in overtly political, STS
traditions, as is showcased by David Hess’ social movement approach to
the sciences, or Andrew Feenberg’s critical constructivism. The conten-
tion is that this outlook is commonly shared in both high church and low
church STS schools.
     Alas, if scientific validity has been shown to be constructed, then we
should expect to find that the bottom-position is just as much of a con-
struction. The emancipatory aspirations, associated with the deconstruc-
tion of scientific truth claims, hinge on the most often implicit assumption
that constructions of the sort are the work of actors with power, money
and prestige. This commonly shared assumption is underpinned by the
Nietzschean/Foucauldian formula: “Power equals truth”. However, if
power can mask itself behind truth-claims, why could it not equally well
dress up as being marginal? A more cautious starting point would be to
assume, that Power takes whatever gestalt, depending on what kind of
claims that society is putting its confidence in for the moment. In the pos-
itivist 1950s, it was the authority of the white-coated doctor that con-
vinced the public to keep on smoking cigarettes. Nowadays, it is often
better to call on the authenticity of a patient group, when trying to sell the
same kind of messages to the public and to regulators. This change of
mood, the Zeitgeist of our time, goes by the name “post-truth”. It calls
for a redirection of the deconstructive thrust. As much effort that has
been placed into deconstructing actor’s truth claims, needs to be put into
deconstructing their claims to be speaking from an imagined below-
position. One might balk at this proposition for good reasons. First, be-
cause of the nihilism that such an endeavor seemingly implies. If claims to
victimhood are found to be as much of a construction as truth claims are
said to be, then the moral fabric of society melts into thin air. Second, be-
cause the appeal to a “below” position was inserted to stabilize a scholar-
ly discourse that was fatally undermined when the weapons of critique
took aim at science, knowledge, and rationality, that is to say, when cri-
tique turned on itself.
     This dilemma, although not entirely new, has been put in a sharper re-
lief by the outburst of post-truth. As is known, the symmetry principle
lays down that actors must be taken on their words when they claim to be
the equal contenders of scientific claim-makers. Not to do so would be
the same as assisting in the marginalizing practices of mainstream, scien-
tific discourse. Differently put, the symmetry principle gives no leverage
100                                                         Tecnoscienza – 10 (1)

for distinguishing between the benevolent outsiders (sheep farmers, in-
digenous people and disabled) and the deplorable ones (anti-vaccination
campaigners, global warming deniers and intelligent design-proponents).
Certainly, scholars have been perfectly capable of making that distinction
anyway, but the criteria for passing those judgements were through-and-
through intuitive. Until recently, nobody noticed it because the same in-
tuitions were shared by everyone in the homogeneous, academic commu-
nity. There was shared and wide agreement upon which outsiders were
the good ones and which were the bad ones. The breaking apart of this
consensus is part and parcel of post-truth. Hence the need for clarifying
the criteria by which actors are claiming to be on the bottom rung on the
ladder. It must be possible to assess those claims, asymmetrically, so that
fake claimants (for instance, white supremacists being excluded from
mainstream media coverage, or corporate sponsored climate change deni-
ers being excluded from contributing to IPCC-reports) can be told apart
from real ones (indigenous people being cornered out by a mining com-
pany, etc.).The criteria by which “fake” and “real” claimants can be dis-
tinguished in the above scenario, are the same principles whereby “false”
is separated from “true”. In order to determine whether or not an actor is
actually speaking from that bottom-rung on the ladder, that he/she is lay-
ing claim to, the possibility of making references to factual states in the
world is indispensable. The attempt to take foothold in a moral register,
instead of an epistemological one, whereby the scholar can pass asymmet-
rical judgements on factual statements without violating the symmetry
principle, has proven to be a dead end. It merely pushes the external ref-
erent one step back in the argumentative chain. Perhaps the referent has
now been put out of sight from the analyst, but that just means that
he/she presupposes a correspondence between his/her discourse and the
world without accounting for it (Marres 2018; Hoffman 2018). The only
move forward that is intellectually consistent is to abstain from making
moral distinctions between self-appointed outsiders’ statements, in lieu of
making epistemological distinctions of those same statements. Differently
put, the whole “basket of deplorables” must be given their full hearing.
Steve Fuller (2017) is alone in “walking the talk”. We should be grateful
towards him for having clarified the price that is to be paid for adopting a
through-and-through symmetrical stance on truth. That no-one else in the
STS community seems to be willing to follow in his footsteps is highly
significant.


Conclusion

With post-truth has come the belated insight that the democratic promis-
es that have hereto been associated with the levelling of all truth claims to
a single, rhizomatic plane are bogus. Everyone from Latour (1993b)1 to
Pellegrino, Söderberg & Milan                                           101
Fuller (2019) are banking on this promise of the bottom-up, in order to
denounce the arrogance and paternalism of the “critical critics”, those
who think they know better than ordinary folk. This is what I elect to call
an “epistemological race to the bottom-up”. What is typically understood
by the expression “race to the bottom” is something quite different: a
global, neoliberal order where nation-states are constrained to lower their
welfare expenditures below that of their competitors on the world mar-
ket, with the aggregated result of a world-wide reduction of welfare
standards. I play on this expression to make the same point in relation to
epistemology. When sociologists are being exhorted to give up on their
theoretical pretences, in place of which they are asked to “follow the ac-
tors”, especially those actors who are the least resourceful – epistemologi-
cally speaking, then this amounts to a call for contracting the analytical
horizon of the social sciences. Astroturfing has brought home a point that
an older generation of sociologists of knowledge were more sensitive to-
wards, namely that the epistemologically weak actors are the ones least
capable of fending off hegemonic worldviews, the ones most likely, in
other words, to be “astroturfed” (Gouldner 1973; Merton 1973). This
point has been lost on a whole generation of social scientists who, under
the towering influence of Michel Foucault, Michel de Certeau, and others
have subscribed to the promises of the bottom-up. Those promises where
once forged out of a general disappointment on the left with the Leninist
party strategy. The resolute counterpoise to the endless race to the bot-
tom that goes by the name “post-truth”, is to revive the old, discredited
notion of the party vanguard, and start to figure out what theoretical and
epistemological lessons it still holds in store for us.



                                    ***


For a Datification from Below

Stefania Milan


The significance of grassroots data epistemologies

    Datafication represents a novel, powerful system of knowledge which
has altered the conditions under which we make sense of the world and
act upon it. It constitutes an unprecedented paradigm shift (see Kitchin
2014), which amplifies the changes brought about by digitalization since
the 1960s. With the automation turn, in particular, much emphasis rests
102                                                         Tecnoscienza – 10 (1)

on the role of artificial agents and machine learning in decision-making.
The belief that artificial intelligence solutions might know better than, for
example, policymakers, is gaining traction also in popular discourse (see
Helbing et al. 2017). With state agencies being often unfit for the chal-
lenge, or simply late in comparison to the corporate sector, users and citi-
zens seem to have lost ground. The balance of power appears to have tilt-
ed for good on the side of large companies and, to a lesser extent, state
institutions – the only organizations with the technical and financial ca-
pabilities to collect, process, make sense, and leverage ever-larger magni-
tudes of information. Meanwhile, however, individuals and groups in-
creasingly engage with data and data infrastructure, fashioning new ways
of being citizens in the datafied society (see also Hintz et al. 2018).
    How does ‘datafication from below’ looks like? How can data gener-
ate citizenship and spur civic engagement? Building on a four-year socio-
logical analysis of how datafication alters democratic practices of partici-
pation, this essay elaborates on the possibilities and conditions of a ‘data-
fication from below’ that can put citizens back into the game – both as
individuals and collective agents. In the age of surveillance capitalism
(Zuboff 2019), ‘big data’ have transformed the ways in which truth claims
are made. Quantification, for one, have taken central stage, foreground-
ing new regimes of measurement (Espeland and Stevens 2008). It is often
presented as a qualitatively superior, infallible way of knowing, which re-
sults in a push towards an objectification of the social world. But not only
machines learn through ‘big data’. Humans do as well, as we are increas-
ingly exposed to narratives and ways of learning and understanding typi-
cal of quantification – regardless of how ‘big’ the data in question is. ‘Liv-
ing with data’ (Kennedy 2018) and the emerging ways of knowing associ-
ated with it are so entrenched in the fabric of daily life to deeply influence
our ways of making sense of interpersonal and spatial interactions – and,
paradoxically, of social change as well. It is the emergence of grassroots
data epistemologies (Milan and van der Velden 2016) and novel ‘data
worlds’ (Gray 2018). Let us look at some examples. Quantification is
progressively entering the repertoire of social movements across the
globe. For example, the #NiUnaMenos (in English, not one [woman]
less) mobilization in Argentina, a country ridden by high rates of gender
violence, embarked in the creation of the national index of sexist vio-
lence, a database ‘from below’ documenting the assassinations of women
in the country, in view of putting the issue on the public debate (Chenou
and Cepeda-Másmela 2019). Black Lives Matter, mobilizing against sys-
temic racism towards black people in the United States, has harbored
‘Data for Black Lives’, a group of activists, organizers and scientists pro-
ducing data on, amongst others, racist violence and police brutality. They
see “data as protest. Data as accountability. Data as collective action” and
are committed to “using data science to create concrete and measurable
change in the lives of Black people” (Data for Black Lives, n.d.). The Ar-
gentinian national index of sexist violence and the activities of Data for
Pellegrino, Söderberg & Milan                                          103
Black Lives are instances of “counter-data action through community-
collected data”, serving the purpose of “provid[ing activists with] evi-
dence” for “claims and experience (…) which in turn they could marshal
as support for their concerted efforts” (Meng and DiSalvo 2018, 1; see al-
so Currie et al. 2016).
To be sure, similar techniques of grassroots production of statistical or
numerical evidence have appeared earlier in the social movements’ reper-
toire, although for a long while they represented merely a tiny and some-
what hidden minority in the social movement ecology. ‘Stat-activists’, for
example, used statistics towards ‘denouncing a certain state of reality’ in
view of changing it (Bruno et al. 2014, 198), such as the post-1968 pro-
tests against unjust imprisonment practices in France (Salle 2014).
    If counting is not entirely new amongst progressive militants, and it
has historically allowed disempowered communities to ‘count’ in society
and make their issues and demands visible (e.g., Gabrys et al. 2016; Rajão
and Jarke 2018), the hype around the possibilities of ‘big data’ has re-
vamped existing imaginaries around quantification and measurement. It
has popularized – and eased, thanks to a wealth of accessible software
tools – data-based interventions and campaigns. These have adopted a va-
riety of tactics, such as crowdsourcing, mapping and data visualization
(e.g., Meier 2015; Gutierrez 2018; Tactical Tech Collective 2013), and
exploited different devices and platforms including citizen-built and citi-
zen-operated sensors (see e.g., Marres 2011). Occasionally, they have
questioned the mainstream narratives associated with data, producing
their own original imaginaries (Lehtiniemi and Ruckenstein 2018) or op-
posing the mainstream imaginaries when these reproduced, for instance,
colonialism (Ricaurte 2019). All in all, big data epistemologies and narra-
tives, also when re-appropriated by citizens and grassroots groups, have
progressively colonized the collective imaginary in a sort of performative
and deeply ideological process in which the socio-cultural and political
understanding of people is demarcated through their exposure to and
practice of material artifacts such as data infrastructure. This should not
surprise us: as neoliberal subjects, Beer (2016, 149) argued, we have a
“cultural interest in numbers, and a culture that is shaped and populated
with numbers”. As a consequence, quantification has permeated both the
activist and public discourse. A number of popular metaphors associated
with datafication and big data (Stark and Hoffman 2019) have supported
this process. For example, the phrase “We are the 99 percent”, propagat-
ed by the Occupy Wall Street mobilization worldwide, is probably the
most fortunate movement slogan of the last decades (Rogers 2012). A va-
riety of actors have contributed to this development. The public admin-
istration sector, for example, making its data available as open data (see
Ruppert 2015), partakes in the creation of empowerment imaginaries of
civic engagement related to the role of data in making discourses. But
what is of interest here is the process through which data becomes a col-
lective story and, even more so, a story of empowerment and agency.
104                                                         Tecnoscienza – 10 (1)


Agency in the Age of Datafication

    To grasp how social actors can exploit datafication to regain or re-
claim agency, we ought to understand what (political) agency consists of
and how it evolves under the pressure of novel data imaginaries. Sociolog-
ically speaking, agency refers to the process of “making sense of the world
so as to act within in” (Couldry 2014, 891). It concerns “intentional, re-
flexive practice oriented to (political) action” (Couldry 2014, 891), in
“domains in which action is both personal and informed” (Feenberg
2011, 1), thus excluding unintentional or routine acts such as breathing
or buying a ticket before boarding a train. What’s more, agency is not
merely an attribute, nor is it given or static. Rather, it is best viewed as a
process (Emirbaye and Mische 1988). More specifically, agency is “the
temporally constructed engagement by actors of different structural envi-
ronments – the temporal-relational context of action – which, through the
interplay of habit, imagination, and judgement, both reproduces and
transforms those structures in interactive responses to the problems
posed by changing historical situations” (Emirbaye and Mische 1988,
970). Agency thus incorporates a fundamental temporal dimension: in
other words, it both evolves over time, and it embeds and makes sense of
various sequential levels. It is rooted in the past, as people continuously
activate past patterns to order their universe and sustain identities over
time. It is projected towards the future, as social actors engage in the im-
aginative generation of future trajectories and possibilities. Finally, it un-
folds in the present, when individuals exercise their ability to make prac-
tical and normative judgements, and act upon them (Emirbaye and
Mische 1988).
    Is agency then altered by datafication, and how? If we adopt
Emirbaye and Mische’s definition, we can see how agency is not entirely
re-written by the paradigm shift of datafication. It is transformed in at
least three ways. Firstly, datafication alters what we may call the ‘social
epistemology’ in which social actors operate, thus touching upon the in-
formed and reflective components of agency. Secondly, it changes how
we mediate and interact with each other, affecting the relational nature of
agency. Finally, it alters how we experience and make sense of the world
around us, modifying thus the situated character of agency. In other
words, datafication has the potential to alter what Emirbaye and Mische
referred to as ‘imagination’ and ‘judgment’. Given these evolving condi-
tions, how can individuals and groups reclaim their agency in the age of
big data?
    One way in which individuals and groups can articulate and reclaim
political agency today goes under the label of ‘data activism’. As I de-
scribed elsewhere (Milan 2017, 2018), data activism embraces those social
mobilizations taking a critical stance towards datafication and mass sur-
veillance. It consists of a variety of sociotechnical practices and tactics
Pellegrino, Söderberg & Milan                                              105
that, through the creative use, appropriation and/or generation of data
and software, interrogate the fundamental paradigm shift of datafication.
Examples include open data activism (Baack 2015), the creative genera-
tion of data for campaigning, through for instance forensic practices
promoting a “disobedient gaze” (Pezzani and Heller 2013) or open
source intelligence tactics (Deutch and Habal 2018), hacking data for the
public good (Schrock 2016), and the development and adoption of coun-
ter-surveillance strategies (Gürses et al. 2016). Data activism is important
for society today because it identifies and disseminates disruptive ways of
making sense of the (social) world and interacting with it, actively coun-
teracting the hyper-positivistic ethos and inevitability surrounding big da-
ta. It points to new roles for active citizens and contributes to the revitali-
zation of the state-citizen relation.
     We can distinguish at least two forms of data activism, positioned
along a continuum. On the one hand, proactive data activism identifies
practices of affirmative engagement with data, exemplified in the
#NiUnaMenos database of gender violence or the efforts by Data for
Black Lives. Proactive data activism takes advantage of both technologi-
cal and legislative innovation and data. Reactive data activism instead
seeks to counter the threats that come along with datafication, most nota-
bly mass surveillance and privacy infringements. Practitioners, for in-
stance, try to popularize security tools for human rights defenders, while
engaging in advocacy to ameliorate legislation and protect citizens. Alt-
hough the boundaries between the two types of data activism are flexible
and particularly permeable, proactive and reactive instances of data activ-
ism tend to embody distinct values and attitudes towards data and datafi-
cation, as well as distinct perceptions of, e.g., state institutions. Proactive
data activists can be seen as tendentially reformist, as they try to marshal
data to ameliorate the output of the state. Reactive data activists, on the
contrary, tend to sport a distrust of institutions, seen as complicit in the
extractive practices of surveillance capitalism. Yet, the two types are not
antithetical: both posit information as a constitutive force in society with
a direct influence on social reality (cf. Braman 2009). Interestingly, while
traditionally confined within the sub-group of sufficiently tech-savvy po-
litical activists, data activism has been steadily expanding its area of influ-
ence over the last decade, signalling that the citizenry at large is becoming
more aware of the possibilities and challenges harbored by datafication
and data infrastructure. But what transforms data into political activism –
or data activism, to be more specific?


Data-logies and the Conditions of Possibility for a Datafication
from Below

   Where does data meet – and possibly becomes a driver of – political
agency? The focus here is not on ‘ordinary forms’ of engagement with da-
106                                                           Tecnoscienza – 10 (1)

ta by non-expert citizens, nor their experiences of datafication (for a de-
tailed analysis of the “layers of conscious experience” in everyday forms
of engagement with data see Kennedy 2018). The analysis instead centers
on motivated – although variably skilled – individuals who consciously
and deliberately engage in ‘intentional, reflexive practice’ (Couldry 2014,
891) at the interplay of data and social change.
    We turn our attention to the meaning-making activities of individuals
and groups approaching data and data infrastructure for social change.
‘Meaning work’, or the ‘interactive process of constructing meaning’
(Gamson 1992, xii) performed by social actors at the micro (=individual)
or meso (=group) level is at the core of taking action in any kind of
movement activity (see also Melucci 1996). However, in the case of data
activism, deeply rooted as it is in the sociotechnical practices of first-hand
engagement with data and data infrastructure, meaning work is en-
trenched in the specific materialities of the datafied society – and in the
critical technical practices (cf. Agre 1997; Dunbar-Hester 2012) they nur-
ture (that is to say, anything from programming to visualizing data to de-
ciding the privacy settings of a smartphone). The peculiar articulation of
meaning work and materialities typical of data activism results in original
declinations of political agency, too.
    I argue that data activism embraces and articulates radical ‘data-
logies’, surfacing the singular meaning work described above. The neolo-
gism takes inspiration from the ancient Greek noun ‘logos’ (λόγος /
λέγω), which means discourse: more broadly, it points to the act of telling
(a story), relating (as in establishing relations), and narrating a reality. Da-
ta-logies, then, refer to ways of thinking about and making sense of da-
ta(fication), with the goal of ‘acting on’ (Kubitschko 2017; Milan forth-
coming). They identify the oppositional and/or disruptive logics associat-
ed with data and datafication from the bottom up. Analytically, data-
logies combine three elements, namely: i) the alternative epistemologies of
data activism, with ii) the socio-technical practices of engagement with
data – from critical technical practice to ordinary-people forms of en-
gagement with data(fication), with iii) the materialities of datafication –
from software to databases to new ways of measurement, categorization
and automation. Data-logies emerge when and where the dimensions of
the cultural (which included the ‘habit’ identified above), the moral (as in
values subtending to collective action), the symbolic, and the emotional
meet the sociotechnical practices of engagement with data. They are sim-
ultaneously individual and collective, but it is in their collective dimen-
sion that they best fulfil their empowerment promises and contribute to
the process of redefinition (and revitalization) of political agency today.
    What makes data-logies emerge, evolve, travel across groups and indi-
viduals, be re-appropriated and ultimately translated into action? We can
identify at least three ‘conditions of possibilities’ for political agency in
the datafied society. The first is critical consciousness. Inspired to the no-
tion of conscientization (conscientização in Portuguese), indicating the
Pellegrino, Söderberg & Milan                                            107
process of ‘gaining consciousness’ as the main outcome of the critical
pedagogy proposed by Brazilian educator Paulo Freire (Freire 1968), crit-
ical consciousness is the result of an approach to education that enables
subjects to become aware of the socio-material conditions of injustice
they live in, and empowers them to translate this sense of injustice into
transformative action. According to Freire, taking action is a constitutive
part of any empowering learning process. Fast-forward to today, fostering
a critical consciousness in the age of datafication is about disentangling
the challenges individuals face in making sense and living with datafica-
tion and surveillance, including risks and opportunities. Understanding
who and what hides in the data shadows is a key step towards transform-
ing one’s surroundings, exercising ‘judgement’ (see above) and fostering
active citizenship.
    The second condition of possibility we can identify has to do with da-
ta literacy – or the ability to find and evaluate critical information on da-
ta-related processes and risks. Data literacy concerns, for example, how to
protect one’s privacy on social media, or how to encrypt email communi-
cations. If opacity and complexity are integral features of datafication, on
account of often obscure industry and state practices and the highly tech-
nical nature of most of these dynamics, data literacy opposes the sense of
disempowerment that datafication harbors. In particular, it could serve
the purpose of ‘demystifying’ the processes subtending to datafication –
from algorithmic personalization to mass surveillance. The specific entan-
glement of sense-making and the material dimension that characterizes
‘acting on’ datafication, however, means that data literacy must include
some sort of first-person engagement with data and/or corrective
measures against surveillance and possibly making one’s hands dirty with
technical practice – from data analysis to visualization. This demystifica-
tion can contribute to lifting the veil that surrounds the data hype and the
associated narratives, ascribing the critical attitude to datafication to the
‘habit’ mentioned by Emirbaye and Mische. Last but not least, the exer-
cise of critical imagination emerges as the conditio sine qua non for exer-
cising citizenship and political agency in the datafied society. Critical im-
agination – a twist on the imagination evoked by Emirbaye and Mische –
has to do with the ability to imagine alternatives with respects to immate-
rial risks (e.g., threats to privacy) and technical practice. Unfortunately,
despite the numerous efforts of the digital rights vanguard (Aouragh et al.
2015; Daskal 2018), datafication-related issues have not yet fully entered
the agenda of contemporary social movements. This brings us back to
what Emirbaye and Mische termed the imaginative generation of future
trajectories in relation to what people primarily care about: health care,
tax, environment preservation. For critical imagination to spread, we
need to articulate new, empowering narratives (as opposed to disempow-
ering ones, such as those often adopted by anti-surveillance activists) able
to help people to translate present (often frustrating) experiences in (em-
powering) future possibilities.
108                                                            Tecnoscienza – 10 (1)


Acknowledgements

    Stefania Milan has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation pro-
gramme (Grant agreement No 639379-DATACTIVE, https://data-activism.net).
She acknowledges that her essay distils two keynotes she was invited to deliver at
the Data Power 2017 conference (Carleton University, Canada, June 23) and at
the 7th STS Italia Conference (University of Padova, Italy, June 15, 2018). She
wishes to thank the organizing committees of both conferences for the opportuni-
ty to connect her work to the themes of both events. Finally, she also wishes to
thank Giuseppina Pellegrino for the invitation to write this piece.



References
Agre, P. E. (1997) Toward a Critical Technical Practice: Lessons Learned in Try-
   ing to Reform AI, in G. C. Bowker, L. Gasser, S.L. Star and W. Turner (eds.),
   Bridging the Great Divide: Social Science, Technical Systems, and Coopera-
   tive Work, Hillsdale, Erlbaum, pp. 131-158.
Aouragh, M., Gürses, S., Rocha, J. and Snelting, F. (2015) Let’s First Get Things
   Done! On Division of Labour and Techno-Political Practices of Delegation in
   Times of Crisis, in “The Fibreculture Journal”, 26, pp. 208–235.
Ashmore, M. (1996) Ending Up On the Wrong Side: Must the Two Forms of
   Radicalism Always Be at War?, in “Social Studies of Science”, 26 (2), pp. 305-
   322.
Baack, S. (2015) Datafication and Empowerment: How the Open Data Move-
   ment Re-Articulates Notion of Democracy, Participation, and Journalism, in
   “Big Data & Society”, July-December, pp. 1-11.
Barber, (2019) Deepfakes are getting better, but they’re still easy to spot, in
   https://www.wired.com/story/deepfakes-getting-better-theyre-easy-spot/,
   May 25, (retrieved May 31, 2019)
Beer, D. (2016) Metric Power, Basingstoke, Palgrave, MacMillan.
Blume, S., Galis, V. and Pineda, A. (2014) Introduction: STS and Disability, in
   “Science, Technology & Human Values”, 39 (1). pp. 98-104.
boyd, d. and Crawford, K. (2011) Six Provocations for Big Data. A Decade in In-
   ternet Time: Symposium on the Dynamics of the Internet and Society, Sep-
   tember 2011, in SSRN https://ssrn.com/abstract=1926431 (retrieved May 30,
   2019).
Braman, S. (2009) Change of State: Information, Policy, and Power, Cambridge,
   MIT Press.
Bruno, I., Didier, E. and Vitale, T. (2014) Statactivism: Forms of Action between
   Disclosure and Affirmation, in “Partecipazione e Conflitto”, 7 (2), pp. 198-
   220.
Pellegrino, Söderberg & Milan                                                 109
Chenou, J. and Cepeda-Másmela, C. (2019) #NiUnaMenos: Data Activism from
    the Global South, in “Television & New Media”, 20 (4), pp. 396-411.
China Daily (2019) China to push for AI courses to be offered at primary, sec-
    ondary schools, http://www.chinadaily.com.cn/a/201903/14/WS5c89bda6a-
    3106c65c34ee983.html (retrieved May 31, 2019).
Clark, R. (1988) Information Technology and Dataveillance, in “Communications
    of the ACM”, 31 (5), pp. 498-512.
Couldry, N. (2012) Media, Society, World: Social Theory and Digital Media Prac-
    tice, Maiden, Polity Press.
Couldry, N. (2014) A Necessary Disenchantment: Myth, Agency and Injustice in a
    Digital World, in "The Sociological Review", 62 (4), pp. 880-897.
Cresswell, T. (2014) Déjà vu all over again: Spatial Science, quantitative revolu-
    tions and the culture of numbers, in “Dialogues in Human Geography”, 4 (1),
    pp. 54-58.
Currie, M., Paris, B., Pasquetto, I. and Pierre, J. (2016) The Conundrum of Police
    Officer-Involved Homicides: Counter-Data in Los Angeles County, in “Big
    Data & Society”, 3 (2), pp.1-14.
Daskal, E. (2018) Let’s Be Careful Out There: How Digital Rights Advocates Ed-
    ucate Citizens in the Digital Age, in “Information, Communication & Socie-
    ty”, 21 (2), pp. 241-256.
Data for Black Lives (n.d.) About Data for Black Lives, in
    http://d4bl.org/about.html (retrieved May 13, 2019).
Degli Esposti, S. (2014) When Big Data Meets Dataveillance: The Hidden Side of
    Analytics, in “Surveillance & Society”, 12 (2), pp. 209-225
Deutch, J. and Habal, H. (2018) The Syrian Archive: A Methodological Case
    Study of Open-Source Investigation of State Crime Using Video Evidence
    from Social Media Platforms, in “State Crime”, 7 (1), pp. 46-76.
Dunbar-Hester, C. (2012) Soldering Toward Media Democracy. Technical Prac-
    tice as Symbolic Value in Radio Activism, in “Journal of Communication In-
    quiry”, 36 (2), pp. 149-169.
ELINET (2015) Literacy in Europe: Facts and figures, in http://www.eli-
    net.eu/fileadmin/ELINET/Redaktion/Factsheet-Literacy_in_Europe-A4.pdf
    (retrieved May 31, 2019).
Emirbaye, M. and Mische, A. (1988) What Is Agency?, in “The American Journal
    of Sociology”, 103 (4), pp. 962-1023.
Espeland, W.N. and Stevens, M.L. (2008) A Sociology of Quantification, in “Eu-
    ropean Journal of Sociology”, 49 (3), pp. 401-436.
Feenberg, A. (2011) Agency and Citizenship in a Technological Society, present-
    ed at the Course on Digital Citizenship, IT University of Copenhagen.
Freire, P. (1968) Pedagogy of the Oppressed, New York, Continuum.
Fuller, S. (2017) Is STS all Talk and no Walk?, in “EASST Review”, 36 (1), pp.
    21-22.
Fuller, S. (2019) Post-Truth: Knowledge as a Power Game, London, Anthem.
110                                                              Tecnoscienza – 10 (1)

Gabrys, J., Pritchard, H. and Barratt, B. (2016) Just Good Enough Data: Figuring
    Data Citizenships through Air Pollution Sensing and Data Stories, in “Big Da-
    ta & Society”, 3 (2), pp.1-14.
Gamson, W.A. (1992) Talking Politics, Cambridge, Cambridge University Press.
Gillespie, T. (2014) The Relevance of Algorithms, in T. Gillespie, P.J. Boczkowski
    and K.A. Foot (eds.), Media Technologies. Essays on Communication, Mate-
    riality, and Society, Cambridge, MIT Press, pp. 167-193
Gitelman, L. (ed) (2013) Raw Data is an Oxymoron, Cambridge, MIT Press.
Gouldner, A. (1973) For Sociology: Renewal and Critique in Sociology Today,
    New York, Basic Books.
Gray, J. (2018) Three Aspects of Data Worlds, in “Krisis: Journal for Contempo-
    rary Philosophy”, 1, pp. 3-17.
Gürses, S., Kundnani, A. and van Hoboken, J. (2016) Crypto and Empire: The
    Contradictions of Counter-Surveillance Advocacy, in “Media, Culture & So-
    ciety”, 38 (4), pp. 576-590.
Gutierrez, M. (2018) Data Activism and Social Change, Basingstoke, Palgrave
    MacMillan.
Harding, S. (1995) ‘Strong objectivity’: a response to the new objectivity question,
    in “Synthese”, 104 (3), pp. 331-349.
Helbing, D., Frey, B.S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y.,
    van den Hoven, J., Zicari, R.V. and Zwitter, A. (2017) Will Democracy Sur-
    vive Big Data and Artificial Intelligence?, in “Scientific American”, (2), Feb-
    ruary 25.
Hintz, A., Dencik, L. and Wahl-Jorgensen, K. (2018), Digital Citizenship in a Da-
    tafied Society, Cambridge, Polity Press.
Hoffman, S. (2018) The Responsibilities and Obligations of STS in a Moment of
    Post-Truth Demagoguery, in “Engaging Science, Technology and Society”, 4,
    pp. 444-452.
Jackson, S.J. (2014) Rethinking Repair, in T. Gillespie, P.J. Boczkowski and K.A.
    Foot (eds.), Media Technologies. Essays on Communication, Materiality, and
    Society, Cambridge, MIT Press, pp. 221-239.
Kania-Ludholm, M. (2018) Online Disconnection and Media Refusal: Toward an
    Agenda for Critical Research, paper preseted at the XIX ISA World Congress
    of Sociology, Toronto, July 15-21.
Kelty, C.M. (2014) The Fog of Freedom, in T. Gillespie, P.J. Boczkowski and
    K.A. Foot (eds.), Media Technologies. Essays on Communication, Materiality,
    and Society, Cambridge, MIT Press, pp. 195-220.
Kennedy, H. (2018) Living with Data: Aligning Data Studies and Data Activism
    Through a Focus on Everyday Experiences of Datafication, in “Krisis: Journal
    for Contemporary Philosophy”, 1, pp. 18-30.
Kitchin, R. (2014) Big Data, New Epistemologies and Paradigm Shifts, in “Big
    Data & Society”, 1 (1), pp. 1-12.
Kubitschko, S. (2017) Acting on Media Technologies and Infrastructures: Ex-
    panding the Media as Practice Approach, in “Media, Culture & Society”, 40
    (4), pp. 629–635.
Pellegrino, Söderberg & Milan                                                  111
Lapowski, I. (2019) Far-Right Propaganda Floods Facebook ahead of EU Elec-
    tions, in “Wired Magazine”, May 22.
Latour, B. (1993a) We Have Never Been Modern, Harvard, Harvard University
    Press.
Latour, B. (1993b) Why Has Critique Run Out of Steam: From Matters of Fact to
    Matters of Concern, in “Critical Inquiry”, 30 (2), pp. 225-248
Lehtiniemi, T. and Ruckenstein, M. (2018) The Social Imaginaries of Data Activ-
ism, in "Big Data & Society", 6 (1), pp. 1-12.
Marres, N. (2011) The Costs of Public Involvement: Everyday Devices of Carbon
    Accounting and the Materialization of Participation, in “Economy and Socie-
    ty”, 40(4), pp. 510-533.
Marres, N. (2018) Why We Can’t Have Our Facts Back, in “Engaging Science,
    Technology and Society”, 4, pp. 423-443.
Maturo, A., Mori, L. and Moretti, V. (2016) An Ambiguous Health Education:
    The Quantified Self and the Medicalization of the Mental Sphere, in “Italian
    Journal of Sociology of Education”, 8 (3), pp. 248-268.
Meier, P. (2015) Digital Humanitarians: How Big Data Is Changing the Face of
    Humanitarian Response, Boca Raton, London and New York, CRC
    Press/Taylor & Francis.
Melucci, A. (1996) Challenging Codes. Collective Action in the Information Age,
    Cambridge, Cambridge University Press.
Meng, A. and DiSalvo, C. (2018) Grassroots Resource Mobilization through
    Counter-Data Action, in “Big Data & Society”, 5 (2), pp. 1-12.
Merton, R. K. (1973) Sociology of Science. Theoretical and Empirical Investiga-
    tions, Chicago, University of Chicago Press.
McNutt, J. and Boland, K. (2007) Astroturf, Technology and the Future of Com-
    munity Mobilization, in “Journal of Sociology & Social Welfare” 34(3), pp.
    165-178.
Milan, S. (2017) Data Activism as the New Frontier of Media Activism, in V.
    Pickard and G. Yang (eds.), Media Activism in the Digital Age, New York,
    Routledge, pp. 151-163.
Milan, S. (2018) Political Agency, Digital Traces and Bottom-up Data Practices,
    in “International Journal of Communication”, 12, pp. 507-527.
Milan, S. (forthcoming) Acting on Data(Fication), in H. Stephansen and E. Treré
    (eds.), Citizen Media and Practice, London, Routledge.
Milan, S. and van der Velden, L. (2016) The Alternative Epistemologies of Data
    Activism, in "Digital Culture & Society", 2 (2), pp. 57–74.
Mosco, V. (2017) Becoming Digital. Toward a Post-Internet Society, Bingley,
    Emerald Publishing.
Pellegrino, G. (2015) Obsolescence, Presentification, Revolution: Sociotechnical
    Discourse as Site for In Fieri Futures, in “Current Sociology”, 63(2), pp. 216-
    227.
Pellegrino, G. (2018) Digitale, im-materiale, mobile, ubiquo: ri-situare la realtà
    della vita quotidiana, in S. Floriani e P. Rebughini (eds.), Sociologia e vita
112                                                            Tecnoscienza – 10 (1)

    quotidiana. Sulla costruzione della contemporaneità, Napoli, Orthotes, pp.
    129-148.
Pezzani, L. and Heller, C. (2013) A Disobedient Gaze: Strategic Interventions in
    the Knowledge(s) of Maritime Borders, in “Postcolonial Studies”, 16(3), pp.
    289-298.
Rabeharisoa, V. and Callon, M. (2004) Patients and Scientists in French Muscular
    Dystrophy Research, in S. Jasanoff (ed.) States of Knowledge: The Co-
    Production of Science and Social Order, New York, Routledge, pp. 234-253.
Rainie, L. and Wellman, B. (2012) Networked: The New Social Operating Sys-
    tem, Cambridge, MIT Press.
Rajão, R. and Jarke, J. (2018) The Materiality of Data Transparency and the
    (Re)Configuration of Environmental Activism in the Brazilian Amazon, in
    “Social Movement Studies”, 17 (3), pp. 318-332.
Ricaurte, P. (2019) Data Epistemologies, Coloniality of Power, and Resistance, in
    “Television & New Media”, 20 (4), pp. 350-365.
Rogers, S. (2012) Anyone Can Do It. Data Journalism Is the New Punk, in "The
    Guardian", May 24.
Ruppert, E. (2015) Doing the Transparent State: Open Government Data as Per-
    formance Indicators, in R. Rottenburg, S.E. Merry, S.J. Park, and J. Mugler
    (eds.), A World of Indicators: The Making of Governmental Knowledge
    through Quantification, Cambridge, Cambridge University Press, pp. 127-
    150.
Salle, G. (2014) Statactivism against the Penal Machinery in the Aftermath of
    ‘1968’: The Case of the French Groupe d’Information Sur Les Prisons, in
    “Partecipazione e Conflitto”, 7 (2), pp. 221-236.
Schrock, A. R. (2016) Civic Hacking as Data Activism and Advocacy: A History
    from Publicity to Open Government Data, in “New Media & Society”, 18 (4),
    pp. 581-599.
Spitzer, M. (2012) Digitale Demenz. Wie wir uns und unsere Kinder um den
    Verstand bringen, Munchen, Droemer Verlag.
Star, S.L. and Griesemer, J.R. (1989) Institutional Ecology, Translations and
    Boundary Objects: Amateurs and Professionals in Berkely’s Museum of Ver-
    tebrate Zoology, 1907-39, in “Social Studies of Science”, 19(3), pp. 387-420.
Star, S.L. and Ruhleder, K. (1996) Steps Toward an Ecology of Infrastructure:
    Design and Access for Large Information Spaces, in “Information Systems
    Research”, 7 (1), pp. 111-134.
Star, S.L. and Bowker, G. (2006) How to Infrastructure, in L.A. Lievrouw and S.
    Livingstone (eds.), The Handbook for New Media, Thousand Oaks, Sage.
Stark, L. and Hoffman, A.L. (2019) Data Is the New What? Popular Metaphors
    & Professional Ethics in Emerging Data Culture, in "Journal of Cultural Ana-
    lytics”, May 2.
Sturken, M. and Douglas, T., 2004, Introduction: Technological Visions and the
    Rhetoric of the New, in M. Sturken, T. Douglas and S. Ball-Rokeach (eds.),
    Technological Visions: Hopes and Fears that Shape New Technologies, Phil-
    adelphia, Temple University Press, pp. 1-18.
Pellegrino, Söderberg & Milan                                                   113
Tactical Tech Collective (2013) Visualising Information for Advocacy, Bangalore,
   Tactical Tech Collective.
Treré, E. (2019) Hybrid Media Activism: Ecologies, Imaginaries, Algorithms,
   London, Routledge.
Vincent, J. (2019) AI researchers tell Amazon to stop selling ‘flawed’ facial recog-
   nition      to    the     police,     in     “The      Verge”,      April      3,
   https://www.theverge.com/2019/4/3/18291995/amazon-facial-recognition-
   technology-rekognition-police-ai-researchers-ban-flawed, (retrieved May 31,
   2019).
Woolgar, S. (1990) Configuring the User: The Case of Usability Trials, in “The
   Sociological Review”, 38 (51), pp. 58-99.
Zuboff, S. (2019) The Age of Surveillance Capitalism, New York, Profile Books.




    1
       The supposed khere of Bruno Latour when he asked the question if
“critique has run out of steam” is on a closer reading just a restatement of his old
polemic against sociology, repackaged as a self-critique.
