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In this paper we extend the use of a relational approach to simulation modelling, a widely used knowledge practice in sustainability science. Among modellers, there is awareness that model results can only be interpreted in view of the assumptions that inform model construction and analysis, but less systematic questioning of those assumptions. Moreover, current methodological discussions tend to focus on integrating social and ecological dynamics or diverse knowledges and data within a model. Yet choices regarding types of modelling, model structure, data handling, interpretation of results and model validation are not purely epistemic. They are entangled with values, contexts of production and use, power relations, and pragmatic considerations. Situated Modelling extends a relational understanding of the world to scientific knowledge production and with that to modelling itself in order to enable a systematic interrogation of these choices and to research social-ecological transformations relationally. To make tangible the situatedness of simulation modelling, we build on existing practices and describe the situatedness of three distinct modelling approaches. We then suggest four guiding principles for Situated Modelling: 1. attending to the apparatus of knowledge production that is socially and materially embedded and produced by e.g. research infrastructures, power relations, and ways of thinking; 2. considering how agency is distributed between model, world, data, modeller in model construction; 3. creating heterogenous collectives which together occupy the formerly individualised subject position; and 4. using agonism as an epistemic virtue to retain and work with significant differentiations of social-ecological dynamics throughout the modelling process.
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“The revolution will not be supervised”: Consent and open secrets in data science
The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production? We hypothesize that a form of boundary violation in data science workplaces—gender harassment—may correlate with the ways humans’ lived experiences are extracted to produce Big Data. The concept of consent offers a useful way to draw comparisons between gender relations in data science and the means by which machines are trained to learn and reason. Inspired by how Big Tech leaders describe unsupervised machine learning, and the co-optation of “revolutionary” rhetoric they use to do so, we introduce a concept we call “techniques of invisibility.” Techniques of invisibility are the ways in which an extreme imbalance between exposure and opacity, demarcated along fault lines of power, are fabricated and maintained, closing down the possibility for bidirectional transparency in the production and applications of algorithms. Further, techniques of invisibility, which we group into two categories—epistemic injustice and the Brotherhood—include acts of subjection by powerful actors in data science designed to quell resistance to exploitative relations. These techniques may be useful in making further connections between epistemic violence, sexism, and surveillance, sussing out persistent boundary violations in data science to render the social in data science visible, and open to scrutiny and debate.
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- Award ID(s):
- 1751314
- PAR ID:
- 10325939
- Date Published:
- Journal Name:
- Big Data & Society
- Volume:
- 8
- Issue:
- 2
- ISSN:
- 2053-9517
- Page Range / eLocation ID:
- 205395172110356
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/20539517211035673
