A presentation for the 4S conference in Sydney, September 2018. Focus on knowledge development during he transition from an analogue to digital data environment and the epistemic risks and possibilities inherent in that process.

1. Big Data, Big World:
Knowledge Development in a Time of Transition
Presenter
Hamish Robertson
Centre for Health Services Management
University of Technology Sydney

2. Contents
• Introduction
• Big data ethics and the implications for social science and policy
• The small data paradigm in retrospect
• Small data ideologies in a big data world
• Epistemic contradictions
• The politics of counting – from analogue to digital
• Digitisation as an epistemic technology
• Conclusion

3. Big data ethics and the implications for social
science and policy
• New and expanding technologies (in the STS sense) whose effects are
being felt by the usual marginal(ised) groups
• Ethics of ‘big data’ technologies is not simply a legal or ethical
concern – consider healthcare, disability, ageing and injury spaces
• Data is not neutral and nor are data technologies ‐ expertise is a
supporting profession => co‐production issue (Jasanoff etc.)
• Speed, breadth, depth and intensification are major concerns –
measuring what? (e.g. Jerry Muller on Metrics/metrification)
• Ethical practice is perfectly viable BUT current arrangements make no
guarantees – Pasquale, O’Neill etc

4. There is no ‘end’ to theory – it’s intrinsic

5. The Small Data Paradigm Seen in the Rear View Mirror
• Hacking’s 1820‐1840 avalanche of numbers ‐ when social quantification
began in earnest
• Who gets counted (or not) often equals a politics of representation ‐
• Where they get counted (crime, education, health) and where not
• How the data is analysed and used – not just reporting
• What analysis methods are applied? – underlying assumptions of same
• Resulting knowledge production paradigms are often highly morally loaded
– social sciences and the ‘deviant’ or the ‘pathological’
(Canguilhem/Foucault) for example
• Data supporting technologies of power – prison, hospital, school

6. Victorian Poverty Mapping and Social Taxonomy

7. Small Data,
Big Data and
their Social
Implications

8. No one is ‘average’ it’s a statistical artefact

9. Small data ideologies: inheriting heuristics from another
age in the digital era
• Nature versus nurture (see eugenics, now genetics)
• Urban versus rural (where does the city really end?)
• Objectivity versus subjectivity (Lorraine Daston, Peter Galison)
• Quantitative versus qualitative (reduction to the mean)
• Culture versus nature (what culture without nature?)
• Economy versus society
• Normal versus pathological (Canguilhem, Foucault)
• Etc….
The datafication of historical social attitudes is both a risk and a
problem of knowledge production and associated critiques.
Role of oppositional dichotomies in data discourse and technology

10. Inverting the
Causal Chain?

11. Social Structure is Deeply Spatially Ordered
• Space is a priori in our world and, consequently, often unexamined or,
preferably, naturalised
• Yet, our social structures are built in bricks and mortar, clean air, clean water,
access to open space etc. etc. ‐ health and medical geography
• The hierarchical position of social groups can be seen in the structure of our
towns and cities, their design and layout, their presences and absences
• The health, ill‐health and disabilities of specific groups are also usually
spatially correlated e.g. rental accommodation and mental illness,
homelessness, public housing blocks etc.
• Invisible or uncommented groups are still physically present even when
notionally absent – strategies of control and marginalization, visibility and
invisibility
• Social policy has always involved spatial strategies – especially concentration
or dispersal e.g. in‐relief versus out‐relief under the UK Poor Law system
(both attitudes and structures were exportable)

12. Space and Place in Geographic Theory
• Space is often represented as Euclidian, abstract, objective, neutral and
utterly quantifiable
• Place is more commonly (yes, there are exceptions) seen as relational,
subjective, partisan, qualitative and even intangible
• Space‐place relations and the politics of gender, ethnicity etc. (who
controls space, who is ‘left’ with place?)
• Policy makers often do not even see the distinction – in many cases not
clear they even know there is one (ageing in place rhetoric)
• Digital quantification (e.g. smart cities) represents both opportunity and
risk – risk of intensification of the spatially instrumental and loss of the
contested place‐based and lived reality
• Traditional (modern) cities not terribly smart – engines of commerce and
inevitable consequences – even the UN says capitalism must go!

13. Spatial and Epistemic Contradictions
• But quantifying beliefs is not the same thing as quantifying knowledge
• (Social) Quantification is not a neutral process – who measures and how
both matter, now under an analogue to digital transition
• Increasingly digital quantification compounds some of these risks
• Spatial quantification and representation are now the norm (satellites,
drones, computer mapping etc.) – civilian and military technologies
increasingly enmeshed ‐> Google Earth/Keyhole/CIA etc.
• Cartography is increasingly ‘map‐ematical’ (Joseph Berry), a mix of
maths and stats in combination with high technology of varying types
and scales (remote sensing, GIS, GPS, Lidar, ‘drones’ etc.)
• How do 19th century beliefs play out in the 21st century under
digitisation? – mapping was power, instruments have their own effects

14. Digital Perception and Reality

15. Digitisation as an Epistemic Technology
• STS extends the concept of technology to include physical, practices,
methods, guidelines, protocols etc. not just the bright, shiny things
• Digitisation can be seen as a physical‐to‐virtual technology (see Yuk Hui)
but, and as a consequence, also an epistemic technology in its own right
• Often seeing an inversion of the ontology‐epistemology equation – reality
framed via technology, not explored by critically informed technical means
• The machine always conditionally captures, reduces, simplifies what
actually exists
• Digitisation, like the small data paradigm, will shape our understanding,
our knowledge production and our understanding of what it is to ‘know’ –
with important ethical and pragmatic implications
• Most of our supporting administrative and epistemic systems are nowhere
near ready for this – law, education, policing, finance etc.

16. Conclusion
• The risks of datafication are under increasing scrutiny – this is a good thing
• The epistemology of data‐informed science and social policy is getting
genuine, intensive attention – but knowledge is contested and political
• The monetisation of data and its consequences are now a part of the
discussion – who benefits and how?
• Spatial science is a pivotal locus for some of this inquiry – important
military/civilian applications and a lack of separation
• Social judgements, now informed by data/measurement/datafication are in
many cases residuals of another, analogue data environment
• The morally loaded heuristics generated by that analogue data era are alive
and well in the present (conservative ideologies)
• This is a significant issue of concern for unequal social regulation via
contemporary and emerging technologies