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Cited by 51Information Systems Theories and ImplementationInterdisciplinary Research and CollaborationDigital Economy and Work Transformation

The digitalisation paradox of everyday scientific labour: How mundane knowledge work is amplified and diversified in the biosciences

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Ribeiro, Barbara, Meckin, Robert, Balmer, Andrew, Shapira, Philip·2023·Research Policy
DOI 10.1016/j.respol.2022.104607
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Abstract

This paper examines how automation and digitalisation influence the way everyday scientific work practices are organised and conducted. Drawing on a practice-based study of the field of synthetic biology, the paper uses ethnographic, interview and survey data to offer a sociomaterial and relational perspective of technological change. As automation and digitalisation are deployed in research settings, our results show the emergence and persistence of what we call ‘mundane knowledge work’, including practices of checking, sharing and standardising data; and preparing, repairing and supervising laboratory robots. While these are subsidiary practices that are often invisible in comparison to scientific outputs used to measure performance, we find that mundane knowledge work constitutes a fundamental part of automated and digitalised biosciences, shaping scientists' working time and responsibilities. Contrary to expectations of the removal of such work by automation and digitalisation, we show that mundane work around data and robots persists through ‘amplification’ and ‘diversification’ processes. We argue that the persistence of mundane knowledge work suggests a digitalisation paradox in the context of everyday labour: while robotics and advanced data analytics aim at simplifying work processes, they also contribute to increasing their complexity in terms of number and diversity of tasks in creative, knowledge-intensive professions.

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  • There is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice.

    “While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice.”

    From Friend or foe? Exploring the implications of large language models on the science system (2025) · cites this paper as Ribeiro et al. 2023

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Research works printed but not linked: 2

  1. Agrawal, A., McHale, J., Oettl, A., 2017. Finding needles in haystacks: artificial intelligence and recombinant growth. In: Agrawal, Ajay, Gans, Joshua, Goldfarb, Avi (Eds.), The economics of artificial intelligence: An agenda. National Bureau of Economic Research.
  2. Balmer, A.S., Bulpin, K., Molyneux-Hodgson, S., 2016. Synthetic Biology: A Sociology of Changing. Palgrave Macmillan, London.

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Authors
Ribeiro, B., Meckin, R., Balmer, A., & Shapira, P.
Affiliations
University of Manchester (GB)
ORCID
Venue
Research Policy
Publisher
Elsevier BV
Published
2023-01
Volume
52
Issue
1
Pages
104607
ISSN
0048-7333 (ISSN-L)
DOI
10.1016/j.respol.2022.104607
OpenAlex
W4292402516
Cited by
51
References
52
Citations by year
2026: 16 · 2025: 13 · 2024: 9 · 2023: 13
FWCI
12.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Research Policy (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Research Explorer (The University of Manchester) (submittedVersion, cc-by)
Funders
Biotechnology and Biological Sciences Research Council (BB/M017702/1)
Topics
  • Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
  • Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
Keywords
synthetic biology, laboratory robots, data standardization, data sharing, technological change, biology research, sociomateriality

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