2nd Call for Papers: The Infrastructures of Socio-Ecological Knowing in the
City
Abstract Deadline: 14th December 2023
We invite you to our workshop on ‘the infrastructures of socio-ecological
knowing in the city’ that will take place on the 11th January 2024, at
King’s College London. The output of this workshop will be proposed as a
special issue, which will be submitted in late Spring of 2024. Contributors to
the workshop will be encouraged to submit to the issue, understanding the
workshop to be a space for developing works in progress, rather than
necessarily presenting complete papers.
Please submit your 200 word proposals by the end of 14th December to
gunes.tavmen@kcl.ac.uk
We have funds to cover (at least partially) the travel expenses for Early
Career Researchers who otherwise don’t have funding. If you need to obtain
visa to travel, let us know and we’ll expedite the decision making process
in your case.
Overview
When the concept of the smart city emerged, one of its primary promises was to
make cities more sustainable. With ubiquitous sensing and real time data
flows, city administrators, we were told, would be able to monitor levels of
urban pollution, energy use and air quality, which would lead to more
efficient and sustainable management of resources. Whilst it is true that
technologies, including cameras. sensors, and more recently AI, have been
effective at recognizing and tracking environmental impacts, there is also
much evidence that highlights the environmental cost of these same
technologies, which rely on energy intensive infrastructures (Monserrate,
2022). Moreover, several scholars have observed that the quantifiable logics
of data collection on this scale (the datafication of pollution, tree
coverage, air quality etc.) does not necessarily lead to meaningful policy
changes or straight forward action (see, for example, Gabrys, 2020).
With the recent progress (and hype) in data and AI technologies, some have
argued that other ways of knowing the city have been eclipsed by the episteme
of data and algorithmic imaginaries, which offer seemingly objective views on
urban processes due to their impressive technical capabilities (Mattern,
2017). Following Louise Amoore’s (2020) work, however, we know that AI and
Machine Learning techniques can be understood as an aperture – or a
perceived opening to new ways of knowing, but also a foreclosing of possible
futures into computational and statistical ways of knowing. So, taking the
urbanists Brenner and Schmid (2015)’s question: ‘through what categories,
methods and cartographies should urban life be understood’?, we instead ask,
‘through what other categories, methods, and technologies could urban
ecology be understood?’
We invite papers that are grounded in the reality of how data and AI
technologies, and the situated socio-political ways they have become embedded
in city governance, have come to shape our understanding of ecological
processes in the city, thus moving us away from the imaginaries of smart city
techno-utopias. We aim to bring together an understanding of the current state
of play, but also to develop future directions for urban-ecological relations
that are guided not by today’s focus on datafication and algorithmic
processing, but by other ways of knowing the city.
Some questions to guide submissions:
• How do sensing and algorithmic technologies shape understandings and
perceptions of socio-ecological systems in cities, and how might they
foreclosure other ways of knowing?
• Which ways of knowing the city are obscured by our society’s focus
on the logics of datafication?
• How are socio-ecological relations made visible through the lens of
digital media? And who are they made visible for?
• What role do digital media platforms play in our understanding of
socio-ecological ways of knowing the city?
• What are the ways of socio-ecological knowing that may be localised,
digital or non-digital that might help in working towards environmental
justice in the urban environment?
Dr Güneş Tavmen
Department of Digital Humanities
King's College London
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