Dynamics of cumulative advantage in science reform: lessons for Open Research from Diffusion of Innovation theory

Activity: Talk or presentationTalk at conference or symposiumScience to science

Description

A growing body of evidence shows that, despite its democratic values and aims, the implementation of Open Research (OR) often results in inequitable outcomes (Ross-Hellauer et al., 2022). Therefore, attention to the equitable implementation of OR has become a key priority for science policy leaders and OR communities (UNESCO, 2021; OCSDnet, 2017; Wilkinson et al., 2016; Carroll et al., 2020). Our work within ON-MERRIT, a Horizon 2020-funded project (2019‒2022), focused on this issue and found that Open Access (OA) publishing has been stratified by the exclusionary nature of the APC model, wherein authors from better-resourced institutions are able to pay higher APCs on average than those from less-resourced ones (Klebel & Ross-Hellauer, 2022), giving them preferential access to higher-cost, higher-impact (Gray, 2020; Tennant & Lomax, 2019); and that better-resourced researchers are better able to engage with policy-makers at the science-policy interface, giving them the opportunity to influence policy development more so than their lesser-resourced colleagues (Cole, Reichmann & Ross-Hellauer, 2023a). Other research has found that open data may widen the academic digital divide due to the infrastructure-dependent, situated nature of open data practices (Bezuidenhout et al., 2017; Johnson, 2018; Klump, 2017), among many other threats (Ross-Hellauer et al., 2022).

That these inequities are rooted primarily in unequal access to resources aligns with Rogers’ (2003b) articulation of unintended consequences within his Diffusion of Innovation theory. In particular, we have documented that it’s not just that inequities exist in and are fostered by the implementation of OR, but that cumulative advantage exists (Ross-Hellauer et al., 2022) – the phenomenon of the rich getting richer, in both economic and academic/scientific terms. This finding is consistent with Rogers’ theory, which holds that innovations often widen existing socioeconomic gaps (2003a). This occurs because 1) early adopters tend to have more resources at their disposal to enable them to adopt higher-cost innovations; 2) those driving innovative change focus on doing so within their existing social networks, which are socio-economically homogeneous; and 3) those that adopt early receive ‘windfall profits’ that widen the gap between them and others (Ibid.). To address these ill effects, Rogers (Ibid.) instructs that 1) attention should be paid to what types of messages are distributed about innovations, to whom they are targeted and how they are communicated; 2) efforts should be made to establish networks of change within less-resourced groups that link them to change agents; and 3) the context-based needs and desires of the less-resourced should be incorporated into the design and diffusion of innovations (Ibid.).

This link, between growing evidence of threats to equity in the implementation of OR, and the rich strand of empirical and theoretical work on Diffusion of Innovation, has to date not been examined. This talk will address this gap, critically engaging with Diffusion of Innovation theory to develop a better, more nuanced understanding of the unintended consequences of OR reform and of strategies to mitigate such negative impacts on equity. In doing so, we will further extend recently published recommendations for equitable implementation of OR (Cole, Reichmann & Ross-Hellauer, 2023b).

References
Bezuidenhout, L.M., Leonelli, S., Kelly, A.H. & Rappert, B. (2017) Beyond the digital divide: Towards a situated approach to open data. Science and Public Policy. 44 (4), 464–475. doi:10.1093/scipol/scw036.

Carroll, S.R., Garba, I., Figueroa-Rodríguez, O.L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J.D., Anderson, J. & Hudson, M. (2020) The CARE Principles for Indigenous Data Governance. Data Science Journal. 19, 43. doi:10.5334/dsj-2020-043.

Cole, N.L., Reichmann, S. & Ross-Hellauer, T. (2023a) The potential of inclusive and collaborative Open Research processes at the science-policy interface. doi:10.31235/osf.io/qzmf6.

Cole, N.L., Reichmann, S. & Ross-Hellauer, T. (2023b) Toward Equitable Open Research: Stakeholder Co-created Recommendations for Research Institutions, Funders and Researchers. Royal Society Open Science.

Gray, R.J. (2020) Sorry, we’re open: Golden open-access and inequality in non-human biological sciences. Scientometrics. 124 (2), 1663–1675. doi:10.1007/s11192-020-03540-3.
Johnson, J.A. (2018) Open data, big data, and just data. Public Administration and Information Technology. 33, 23–49. doi:10.1007/978-3-319-70894-2_2.

Klebel, T. & Ross-Hellauer, T. (2022) The APC-Effect: Stratification in Open Access Publishing. doi:10.31222/osf.io/w5szk.

Klump, J. (2017) Data as Social Capital and the Gift Culture in Research. Data Science Journal. 16 (0), 14. doi:10.5334/dsj-2017-014.

OCSDnet (2017) Open Science Manifesto. 2017. OCSDnet. https://ocsdnet.org/manifesto/open-science-manifesto/ [Accessed: 22 December 2022].

Rogers, E.M. (2003a) Consequences of Innovations. In: Diffusion of Innovations. 5th edition. New York, NY, Free Press. pp. 457–471. https://www.simonandschuster.com/books/Diffusion-of-Innovations-5th-Edition/Everett-M-Rogers/9780743222099.

Rogers, E.M. (2003b) Diffusion of innovations. 5th ed. New York, Free Press.

Ross-Hellauer, T., Reichmann, S., Cole, N.L., Fessl, A., Klebel, T. & Pontika, N. (2022) Dynamics of cumulative advantage and threats to equity in open science: a scoping review. Royal Society Open Science. 9 (1), 211032. doi:10.1098/rsos.211032.

Tennant, J.P. & Lomax, D.R. (2019) An overview of open access publishing in palaeontology. Palaeontologia Electronica. 22 (2), 1–10. doi:10.26879/968.

UNESCO (2021) UNESCO Recommendation on Open Science.p.34. https://unesdoc.unesco.org/ark:/48223/pf0000379949.locale=en.

Wilkinson, M.D., Dumontier, M., Aalbersberg, Ij.J., Appleton, G., Axton, M., et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. 3 (1), 160018. doi:10.1038/sdata.2016.18.
Period8 May 2023
Event title21st Annual STS Conference Graz 2023: Critical Issues in Science, Technology and Society Studies
Event typeConference
LocationGraz, AustriaShow on map
Degree of RecognitionInternational

Keywords

  • open science
  • open research
  • matthew effect
  • cumulative advantage
  • diffusion of innovation