4 steps to cultivating a more inclusive research landscape
March 31, 2021 | 6 min read
By Max Voegler, PhD
Improving data collection policies and removing structural barriers are key to boosting diversity in research
Global progress around gender equality in the research ecosystem is moving in the right direction, though not as fast as we would hope. However, when tackling the issue of racial and ethnic diversity, defining goals and measuring progress seem much more difficult. To make real change, evidence-based and measurable approaches are needed. Yet currently, there are limits to defining categories and collecting data that are comparable, especially when scaling up to a global view.
This systemic issue calls for a systemic approach. Elsevier and the Royal Society of Chemistry(opens in new tab/window) convened a series of workshops on Race and Ethnicity in the Global Research Ecosystem with a selection of research funding organizations. A central question they addressed was: How are organizations supporting national transformation on issues around diversity, race and ethnicity?
In her opening remarks, Elsevier CEO Kumsal Bayazit says:
This is the only way to create a more inclusive publishing system: by adopting a common approach to combat bias and create greater transparency as a sector.
1. Clarity is critical
Data collection plays an important role in informing initiatives and monitoring progress, but collecting the right, comparable data is the biggest hurdle. And while work on inclusion and diversity is already grounded in evidence, issues remain around the language of race and ethnicity and its implications.
Because there is currently no comprehensive global list of mutually exclusive ethnoracial groups, developing a usable framework for data collection is key for progress. It must be understood, however, that terminology decisions will inevitably be subjective and not straightforward — and that respondents will need explicit guidance and clear direction.
Questions for organizations to consider include:
How will the data be used? What level of detail is needed, and what is manageable?
Which approach(es) might be particularly relevant for, or most clearly understood by, the population surveyed?
How can organizations set targets, work with government and monitor progress?
Meanwhile, it’s hard to know what “good” looks like. For example, gender data is currently used as a baseline to develop action plans, but it’s important to consider who accepts and rejects targets and what implications this has.
Much has been done on gender as a starting point for broader diversity work. It might be impossible to develop a single schema for collecting race and ethnicity data across the board, but the scientific community can work together to develop useful guidelines. An example of a recommended schema is the Joint commitment for action on inclusion and diversity in publishing(opens in new tab/window), led by the Royal Society of Chemistry, which counts 46 international publishing organizations as signatories. Participants are working to develop a recommended approach for collecting data that will be global, meaningful, appropriate and accepted.
2. Representation matters
Internal diversity is key to driving progress. Within organizations, those tasked to work on diversity dynamics need to consider how to build diverse teams: institutions that are truly taking diversity seriously need to empower those most directly concerned, creating a space that is receptive and empowered for change. When working on diversity and decolonization, it is also helpful to think about how to show the diversity of participants in the research system, many of whom are currently invisible: role models don’t need to be “research superheroes,” but rather people of diverse races and ethnicities that are ordinary members of the research community.
However, it’s important to remember that though marginalized communities must be included, it’s not their role to fix the system — everyone needs to support change. For example, in many disciplines, Black scholars are represented at a lower rate. Those colleagues are often overwhelmed with requests for participation in working groups and panels while at the same time needing to focus on their careers. Questions for organizations to consider include:
How can we improve diversity internally within our organizations? Have we consulted with the right employees groups?
Do the images and messages used in internal and external channels reflect the diversity the organization is working towards?
Participants in this workshop shared examples of initiatives they have trialed to improve diversity internally, including reverse mentoring and hiring associates with no university degrees or from underprivileged backgrounds. However, organizations need to be conscious to not design policies “onto” others and instead include everyone in the conversation; this will require an approach to collecting and analyzing qualitative data as a way of gathering stories and including a greater diversity of voices.
3. Minimize bias and increase diversity in peer review
Underrepresentation is a problem. Data shows that ethnic minority groups are underrepresented in applying for grants and less likely to get them and less likely to be in the studentship population and to participate in peer review. But despite evidence of underrepresentation, there is a dearth of data that can be used to inform action. The peer review process lies at the foundation of science and is at the heart of how both academic publishers and research funders operate. However, unconscious bias is a major problem in peer review, and this bias must be minimized. It is particularly challenging to tackle unconscious bias in individual review reports, and to improve trust in peer review. Some organizations are already working on diversifying review panels and advisory groups, setting targets to ensure researchers are fairly evaluated and given fair chance to participate in the process. Still, to increase transparency, more can be done by publishers and funders alike to make data available on the makeup of panels and selection committees.
4. Collaborations can lead to long-lasting change
No single organization can fix these complex issues, and the work on race and ethnicity is hard and complex: action needs to focus on both removing systemic barriers at the institutional level and lifting individuals. Workshop participants recognized that there is significant value in a collaborative approach that shares best practices and coordinates collective actions. For example, focusing on a pilot area such as the chemical sciences, where a third to half of researchers are internationally mobile, could be a good starting point to pilot interventions. Organizations are invited to enter this conversation and take steps as a community to improve diversity and inclusion in the global research landscape.
These are the publications and other resources participants shared during the workshops.
Elsevier: Example of transparent editorial board composition(opens in new tab/window) (Journal of Food Composition and Analysis) and Elsevier's journal now displaying editors’ gender in support of diversity(opens in new tab/window)
European Institute for Gender Equality: Gender Equality in Academia and Research toolkits(opens in new tab/window) : Legislative and policy backgrounds
NIH Scientific Workforce Diversity Seminar Series (SWDSS): Outcomes from NASEM’s June 2021