Throughout its history, the United States has pursued two main objectives through its Federal Indian law and policy: the assimilation of the country’s First Peoples and the dispossession of their land. Both objectives have worked toward the ultimate goal of erasing Indigenous Peoples [Newland, 2022]. Laws and policies serving this goal have repercussions across all aspects of the lives of Indigenous Peoples, whether through influence on our representation in popular culture (or lack thereof) or on the research initiatives and funding opportunities that are accessible to us.
Such policies are clearly not a thing of the past; they continue to be part of an ongoing process of settler colonialism that is furthering the erasure of the First Peoples and first scientists of this country. Recent polling by the Native-led nonprofit organization Illuminative indicates that 78% of Americans “know little to nothing about Native Americans and a significant portion believe that Native Peoples must be a dwindling population because they do not see, hear, or read about Native Peoples.” These sentiments are not new and echo, a century later, those embodied in photographer Edward Curtis’s early 20th century “Vanishing Race” project, wherein he sought to document Native American lives and cultures, which he falsely thought would fade from existence.
The erasure of Indigenous Peoples has long led to their underrepresentation across academic fields, especially in the environmental sciences. This field continues to be among the least diverse in the United States, with more than 67% of degrees awarded to white students in 2019 and only 20% awarded to Latinx, Asian, Black, Indigenous, and Pacific Islander students combined, according to U.S. Department of Education data compiled by Data USA. The geosciences exhibit similar trends in their lack of diversity.
All the work that environmental scientists, ecologists, and geoscientists do in the United States inherently involves Indigenous lands, so it is critical that scientists examine how this work is done, who is represented through it, and who has access to do the work in the first place.
In a recent study, Chen et al.  detail patterns that suggest there are systemic racial disparities in the success rates of proposals funded by the National Science Foundation (NSF) across all its directorates. The authors examined funding rates for principal investigators (PIs) from 1996 to 2021 using NSF data extracted from publicly available annual reports. Notably few, or even absent for certain years, are data on Indigenous scholars. These omissions are no fault of the authors, but rather occur because current NSF reporting structures lead to nonreporting of numbers that are deemed “too small” (n < 10) and because of a broader lack of representation of Indigenous scholars in academia.
Indigenous exclusion from NSF data is disheartening but not surprising. Implicit biases, stereotypes, and the overall invisibility of Indigenous Peoples in U.S. popular culture—together with explicit policies—influence how and even if we are represented in data. Invisibility in data, in turn, negatively affects the material support that Native communities receive. Moreover, such invisibility is dehumanizing and frequently prevents Native communities from having a seat at the table when important decisions that affect these communities are made. For instance, when we see that our numbers are “too small to report,” it can be easy for early-career researchers to ask, Do we even belong here at all? As a third-year Ph.D. candidate, I have asked myself this question many times. Often, it is only when I am in Indigenous spaces, such as at the American Indian Science and Engineering Society National Conference, that I find support and assurance that I can succeed in my field.
Eradicating systemic racism in academia must involve appropriate, accurate, and accessible data on Indigenous representation. Without accurate data about American Indian, Alaskan Native, Native Hawaiian, and Pacific Islander PIs, we cannot even begin to understand or mitigate the racial disparities that affect Indigenous groups.
Including Indigenous Communities
Data can both empower and marginalize groups, so it is increasingly necessary for institutions and individuals with privilege to assess how they are engaging with data, who they might be leaving out of conversations related to it, and why there are inequities in the first place. In their study, Chen et al.  note that data for American Indian, Alaskan Native, Native Hawaiian, and Pacific Islander groups are routinely not reported, which complicates accurate calculations of funding rates. What does it say when a federal agency that plays such a pivotal role in steering and implementing national research priorities leaves out groups of people entirely in its reporting of where funding is going (or not going)?
Invisibility of Indigenous Peoples is not just a problem within NSF funding data. Across disciplines, tribes are often left out of the planning and execution of research on Indigenous lands, and as a result, they may lack detailed information to make informed health decisions, for example, or access to data that can help with stewardship of natural resources across landscapes. As scientists shape and explore research questions around work on Indigenous lands, we must not leave out the communities from which this work is often derived.
Achieving representation within academic settings and research initiatives is a positive step but will not resolve issues of inequity without parallel shifts in the allocation of funding and resources. However, the current funding mechanisms that power academic institutions reinforce the same criteria that have characterized who has historically been most successful in those spaces: cisgender, heterosexual, white males.
Chen et al.  found that white PIs are funded by NSF at higher rates than any other racial group. This trend could result from biases in the selection process as well as from compounding disparities between American Indian, Alaskan Native, Native Hawaiian, and Pacific Islander PIs and white PIs such as education history and career longevity.
It’s hard not to wonder whether any funded proposals by non-Indigenous researchers co-opt Indigenous data and knowledge to their benefit, especially as a means to enhance the stated “broader impacts” of their work. Is funding prioritized for those wishing to account for Traditional Ecological Knowledge or to work with tribal communities but not for Indigenous researchers themselves?
It is important to acknowledge how Indigenous Knowledge systems are increasingly relevant across all disciplines, but we also must make sure that researchers are not benefiting from that knowledge in extractive or harmful ways. Indigenous communities maintain Ancestral Knowledge, which contributes to the greater body of knowledge helping us all understand Earth and environmental sciences. Excluding Indigenous populations, both from funding and by not acknowledging existing Ancestral Knowledge, limits the growth of this understanding. Who is getting funded to do work that engages Indigenous communities matters.
Ending Indigenous Erasure
Indigenous Peoples have resisted colonial, racist, and inequitable processes that have worked for hundreds of years to dispossess communities of their land and Traditional Knowledge. The resilience to persist against these systemic barriers is not new to us, and we will keep pushing against them.
In addition, there are entities that can prove their allyship by helping to remove such barriers and ensuring they aren’t created in the first place. For agencies and institutions like NSF, this allyship can start with accurate and complete reporting of data. Such organizations can and should do better than to perpetuate the myth and harmful stereotype that the numbers of Indigenous Peoples are too small to matter.
Specific recommendations for moving forward include the following:
1. Track trends for all groups regardless of the size of the data set. In its 2019–2020 Biennial Report to Congress, the Committee on Equal Opportunities in Science and Engineering, an advisory committee to NSF, highlighted a point made by Wullert et al. : “Small numbers cannot be a rationale to stall progress. Concluding that little can be said with limited data renders underrepresented groups more invisible and creates a roadblock to meaningful changes. To create lasting and impactful changes, organizations should be willing to analyze small numbers…and hold themselves accountable to making small numbers grow.” Rather than leave out groups entirely, NSF should consider devising small-number statistical methods to analyze changes in funding accurately for these groups and report raw numbers.
2. Adopt the tenets of Indigenous data sovereignty and CARE principles. Indigenous data sovereignty affirms the rights of Indigenous Peoples to determine the means of collection, access, analysis, interpretation, management, dissemination, and reuse of data (CARE) [Kukutai and Taylor, 2016]. This sovereignty derives from the inherent rights of self-determination as set forth in the United Nations Declaration on the Rights of Indigenous Peoples and mandates that data be used to support and enhance Indigenous Peoples’ collective well-being. CARE principles similarly seek to ensure collective benefit, authority to control, responsibility, and ethics regarding the use of Indigenous data [Carroll et al., 2020]. NSF, other funding agencies, and individual scientists should incorporate Indigenous data sovereignty and CARE principles into project evaluations.
3. Include Indigenous experts in assessments of projects related to Indigenous Knowledges. If a funding proposal includes the use or analysis of Indigenous data, knowledge, or lands, Indigenous experts should be involved in assessing that proposal. Funding agencies could convene panels of cultural experts or community members to assess these types of proposals properly. These individuals should be compensated appropriately for contributing their time and knowledge.
4. Increase the number of awards available to underrepresented groups to bridge funding gaps. This recommendation echoes a point made by Chen et al.  that the number of awards needed to bridge some racial disparities is small and might be overcome with targeted programs for those groups. For work that engages Indigenous communities, the additional considerations of Indigenous data sovereignty and the use of Indigenous lands and Traditional Ecological Knowledge should be addressed by, for example, implementing CARE principles or including Indigenous experts on review panels as mentioned above.
Indigenous Peoples have been expert data collectors and keepers for thousands of years. We recognize the value of data, and we most definitely notice when our stories are absent in reported data. These facts will remain true regardless of power structures that try to make it otherwise. Through our own persistent efforts and those of allied individuals and institutions, however, we can continue changing these power structures to stop Indigenous erasure, in funding data and beyond.
Carroll, S. R., et al. (2020), The CARE principles for Indigenous data governance, Data Sci. J., 19(1), 43, http://doi.org/10.5334/dsj-2020-043.
Chen, C. Y., et al. (2022), Systemic racial disparities in funding rates at the National Science Foundation, eLife, 11, e83071, https://doi.org/10.7554/eLife.83071.
Kukutai, T., and J. Taylor (Eds.) (2016), Indigenous Data Sovereignty: Toward an Agenda, ANU Press, Canberra, www.jstor.org/stable/j.ctt1q1crgf.
Newland, B. (2022), Federal Indian boarding school initiative investigative report, U.S. Dep. of the Inter., Washington, D.C., www.bia.gov/sites/default/files/dup/inline-files/bsi_investigative_report_may_2022_508.pdf.
Wullert, K., S. Gilmartin, and C. Simard (2019), The mistake companies make when they use data to plan diversity efforts, Harvard Bus. Rev., 16 Apr., hbr.org/2019/04/the-mistake-companies-make-when-they-use-data-to-plan-diversity-efforts.
McKalee Steen (email@example.com) is a member of the Cherokee Nation and a doctoral student at the University of California, Berkeley