University Rents Enabling Corporate Innovation
This article explores the role of unrecognised labour in corporate innovation systems via an analysis of researcher coding and discursive contributions to R, one of the largest statistical software ecosystems. Studies of online platforms typically focus on how platform affordances constrain participants’ actions, and profit from their labour. We innovate by connecting the labour performed inside digital platforms to the professional employment of participants. Our case study analyses 8,924 R package repositories on GitHub, examining commits and communications. Our quantitative findings show that researchers, alongside non-affiliated contributors, are the most frequent owners of R package repositories and their most active contributors. Researchers are more likely to hold official roles compared to the average, and to engage in collaborative problem-solving and support work during package development. This means there is, underneath the ‘recognised’ category of star researchers who transition between academia and industry and secure generous funding, an ‘unrecognised’ category of researchers who not only create and maintain key statistical infrastructure, but also provide support to industry employees, for no remuneration. Our qualitative findings show how this unrecognised labour affects practitioners. Finally, our analysis of the ideology and practice of free, libre and open source software (FLOSS) shows how this ideology and practice legitimate the use of ‘university rents’ by Big Tech. In conclusion, we argue that existing mechanisms are insufficient to ensure these digital commons’ sustainability: FLOSS needs broader systemic support.
Authors
- Mathieu O’Neil (ORCID: https://orcid.org/0000-0002-3537-9036)
- Stefano Zacchiroli
- Xiaolan Cai
Institutions
- Télécom Paris (FR)
- University of Canberra (AU)
Publication Details
- Journal
- Journal of Quantitative Description Digital Media
- Published
- 2025-12-31
- DOI
- https://doi.org/10.51685/jqd.2025.025
- Primary Topic
- Computational and Text Analysis Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- International Association for Media and Communication Research
- Nanyang Technological University
- University of New South Wales
- Centre National de la Recherche Scientifique