Table 2.

Summary of identified areas of concern for equity in Open Science.

aspect of Open Science	area for concern	group(s) most affected
general factors	costs of participation: Open Science is resource-intensive in terms of infrastructure, support, training	less well-resourced institutions and regions
	political agendas: Open Science requires political will, but political agendas shape Open Science implementation. Especially where economic growth is a stated ambition, this may be problematic	regions and institutions without such political backing, or where political goals promote inequitable Open Science implementations
	neoliberal logics: Open Science seen as potentially entrenching structures and ideologies of neoliberal commodification and marketization of research knowledge as an economic resource to be exploited rather than as a common good for the well-being of humanity	science per se, but especially those disciplines and researchers that do not fit this agenda
Open Access	discriminatory business model: APC-based OA is exclusionary and risks stratifying authorship patterns	less well-resourced researchers, institutions and regions. May also impact specific demographics, including women
	predatory publishing: limited issue which nonetheless primarily adversely affects non-dominant groups	authors from developing nations and early career researchers
Open Data and FAIR Data	situatedness of data practices: data practices are highly context-dependent, meaning one-size-fits-all policies risk privileging some disciplines	qualitative researchers and disciplines
	cumulative nature of data inequalities: creating and exploiting Open Data is strongly linked to access to infrastructure and data literacy	less well-resourced researchers, institutions and regions
	citation advantages of Open Data: Open Data seems linked to increased citations and hence early adopters benefit (Matthew effect)	less well-resourced researchers, institutions and regions
Open Methods and Open Infrastructure	transparency as a benchmark for quality: open methods require additional training, effort, infrastructure. Well-resourced and high-status actors may potentially have an advantage	less well-resourced researchers, institutions and regions
	reproducibility as a sine qua non for research: relatedly, meanings and limits of openness not uniform across disciplines. Uncritically extending quantitative standards methodologies may obscure necessary interpretive work or further devalue qualitative approaches	qualitative researchers and disciplines
	platform-logic of Open Science: reliance on privately owned platforms may frustrate the aims of Open Science and increase surveillance capitalism in academia	science as a whole
	lack of reward structures for contributions to open infrastructure: Open Science seems at risk if it relies on closed and proprietary systems; yet open infrastructures often rely on short-term project funding or volunteer labour which is not properly rewarded within current incentive structures	early career researchers
Open Evaluation	open identities peer review: peer review where reviewers are de-anonymized may either by discourage full and forthright opinion or opening especially early career reviewers to potential future reprisals from aggrieved authors later on	erly career researchers, others from non-dominant groups
	suitability of altmetrics as a tool for measuring impact: altmetrics criticized for: lack of robustness and susceptibility to ‘gaming’; disparities of social media use between disciplines and geographical regions; reliance on commercial entities for underlying data; indicating ‘buzz’ rather than quality; underrepresentation of data from languages outside English; exacerbating tyranny of metrics	all, especially non-English language research and areas where social media use is less pronounced
Citizen Science	logics of participation in Citizen Science: evidence of biased inclusion in populations invited to participate; potential for data extraction absent anything else to echo colonial exploitation	the public, especially marginalized groups
interfaces with society, industry, policy	resource-intensive nature of translational work: making outputs open is not enough to ensue uptake and societal impact. The importance of (resource-intensive) translational work means richer institutions and regions may still dominate policy conversations	less well-resourced researchers, institutions and regions
	privileging of economic aims: the terms on which Open Science engages industry is asymmetrical, perhaps reflecting the importance of economic growth as a key aim. Industry is free to participate (or not) in open practices, as it suits them	science as a whole, but especially those domains not easily exploited by commerce
	exclusion of societal voices: Open Science's terms of inclusion of publics is accused of ‘instrumentalism’ and asymmetry (experts/public)	the public