Academia likes to imagine itself as a space apart from the market, a place where ideas matter more than profit, where curiosity leads and knowledge serves the public good. But anyone working inside the university today knows that this ideal is fiction. Academia is not just influenced by capitalism, it is structurally entangled with it. And that entanglement is reshaping what knowledge is produced, who gets to produce it, and who ultimately benefits.

Under a capitalist model, productivity becomes the dominant metric of worth. Publications are counted, grants are totaled, citation indices are ranked, and “impact” is reduced to numbers that fit neatly into spreadsheets. The slow, careful, relational work that much scholarship actually requires, mentorship, community collaboration, long-term fieldwork, interdisciplinary thinking, teaching that changes students’ lives, often becomes invisible because it is difficult to quantify or monetize.

This logic also narrows the kinds of questions we are encouraged to ask. Research that promises quick results, technological applications, or marketable outcomes is prioritized, while work that is critical, exploratory, or socially uncomfortable is labeled as too “niche”, “unfundable", or "this study doesn't have enough background." (hm maybe because it's never been done). Students are not trained as scholars but rather academics, where they  design projects that fit short grant cycles and tidy timelines, rather than projects that follow unfolding biological, ecological, or social processes.

Reframing this mindset would not weaken academia, it would expand it, and make knowledge acquisition richer. It would open entirely new avenues of research that are currently foreclosed by rigid timelines, funding structures, and career precarity. Many of the most meaningful questions in science and the social sciences are longitudinal by nature. They require patience, continuity, and trust built over years, sometimes decades.

In my own work on maternal care, this tension is especially clear. Maternal behavior, development, and caregiving strategies do not reveal themselves neatly within a dissertation window, a two-year grant, or a single field season. Yet under current academic constraints, limited funding, compressed timelines, and scarce opportunities for early-career researchers to return to the field, longitudinal studies led by young scholars with novel questions are rarely possible. These researchers are often encouraged to scale down their ambitions, retrofit their questions to existing datasets, or abandon longitudinal designs altogether, not because the science is weak, but because the system cannot accommodate it.

What is lost in this process is not just individual projects, but entire ways of knowing. We lose insights into change over time, intergenerational processes, slow adaptation, and care systems that only become visible through sustained engagement. We also lose the creativity of early-career researchers, whose ideas are often the most theoretically adventurous but the least supported by existing power structures. This erosion of long-term, relational scholarship reflects a broad failure within academia: a systematic distancing from Indigenous knowledge systems that center patience, reciprocity, and learning across generations. Going back to the roots of scholarship means confronting this history—unlearning extractive modes of inquiry and relearning how to engage with knowledge as something cultivated in relationship, not extracted on a deadline.

A more progressive academia would recognize that good science does not always move quickly, and that innovation often emerges from long-term commitment rather than rapid output. It would invest in continuity—supporting early-career researchers in returning to field sites, maintaining long-term datasets, and building research programs that grow organically rather than reset every funding cycle. Or in other terms, it would invest in the fundamentals of indigenous knowledge. Something that has been buried since the beginning of western academic practice.

Also academic labor must be rethought. Universities increasingly rely on precarious workers, adjuncts, graduate students, postdocs, whose intellectual contributions sustain institutions but whose livelihoods remain unstable. Or, promise positions that cannot be upheld, leaving these scholars with a debt of opportunity. Passion is quietly substituted for pay. Devotion to students, collaborators, and communities is used to normalize burnout. The message is subtle but persistent: if you truly care about this work, you will accept insecurity, overwork, and silence.

These structures disproportionately harm scholars from marginalized backgrounds, who are more likely to be filtered out by funding gaps, visa precarity, unpaid labor expectations, and opaque norms. When academia adopts the logics of capitalism, it reproduces inequality rather than challenging it.

Getting out from under capitalism’s thumb does not mean rejecting rigor or excellence. It means redefining them. Excellence should not mean exhaustion. Productivity should not require precarity. Impact should not be measured solely in grants won or papers counted, but in how knowledge deepens understanding, strengthens communities, and reshapes how we care for one another and the world we inhabit.

If academia truly wants to be progressive, it must go back to the roots of scholarship and indigenous ways of knowing. This means reclaiming curiosity over productivity, depth over speed, and responsibility over branding. Scholarship was never meant to be optimized for quarterly returns or compressed into grant cycles that ignore the realities of biological, social, and ecological time. Returning to these roots would allow research to be shaped by the questions themselves, especially those that demand longitudinal, relational, and care-centered approaches, rather than by the constraints of funding structures and academic timelines.

Periods of political and economic instability have a way of clarifying what is often obscured in everyday academic life: scholarship is a privilege. The capacity to engage in sustained intellectual work, to read, write, analyze, and theorize, depends on conditions that are not universally available. It requires time, financial stability, institutional support, and personal safety.

This is a reality I, and many American academics/scholars, are now barely reckoning with. For my life, I moved through academia without questioning whether scholarship itself was accessible to me. I travel to other spaces and interact with environments that are novel to me. That ease was not accidental; it was made possible by structural privilege, citizenship, relative economic stability, and institutional protection, that insulated my work from the most immediate consequences of political and economic instability.

When people face precarity, displacement, censorship, or violence, scholarship is no longer an accessible or even imaginable pursuit. Yet academic systems often proceed as if research exists outside these realities, expecting uninterrupted productivity regardless of crisis. This quietly privileges those whose lives are least disrupted, while pushing others—often scholars from the very regions and communities being studied—out of academic participation.

In this sense, academia not only reflects inequality; it reproduces it. Knowledge production becomes centralized in spaces of relative stability, while insights from places under strain are filtered, mediated, or lost. What remains is an incomplete understanding of the world, shaped by who could afford to keep thinking when others could not.

Acknowledging scholarship as a privilege is not an act of much. It is a necessary step toward honesty. If academic work is to be ethical, it must confront the conditions under which it is possible—and imagine ways to support knowledge-making grounded in collective care and responsibility.

I want to be explicit about my position. I have lived an extraordinarily privileged life. Even when academia has felt difficult or precarious, those challenges do not meaningfully compare to the lived realities of other people—within this country or across the world, now or at any point in history. Any constraints I experience exist within a system that still affords me safety, choice, and voice. Naming this is not self-castigation, but an acknowledgment of scale: my experience does not hold a candle to the realities that make scholarship inaccessible, or unimaginable, for so many others.

For decades, alloprimates, especially, macaques, marmosets, chimpanzees, and baboons, have occupied a central role in biomedical research. Their genetic and physiological proximity to humans made them appear to be ideal laboratory stand-ins for human biology. That assumption helped shape the infrastructure of modern biomedical science: breeding colonies, laboratory testing pipelines, and regulatory systems built around animal experimentation.

In this essay, I use the term alloprimate to refer to primates other than humans. The term emphasizes that humans are not separate from the primate order but part of it.

But science does not stand still, and neither should our ethical frameworks. As biomedical technologies advance and our understanding of primate cognition deepens, a question that once lived at the margins of academic debate is now entering federal policy discussions: Do we still need to use alloprimates in biomedical research?

Many researchers argue that we do,or at least that the model cannot be abandoned quickly. Their position is typically framed as pragmatic: alloprimates remain the best available model for certain complex biological processes, and until alternatives are fully validated, removing them from the research pipeline could slow medical progress. But the persistence of alloprimate testing is not primarily a scientific necessity. It is the product of institutional inertia, economic investment, and a research system built around animal experimentation.

Theoretical Pros and Cons

The most persistent argument for using alloprimates is their biological similarity to humans. Scientists have long pointed to evolutionary proximity as evidence that alloprimates may provide better predictors of human biological responses than other animal models, a justification that has anchored their use in biomedical research for over a century (Honess, 2016).

This work has produced important medical advances. Vaccines for polio, yellow fever, and hepatitis B, along with progress in HIV treatment and neuroscience, were partly developed through studies involving alloprimate subjects. Supporters of the model argue that for certain biological systems, particularly complex immune responses, monoclonal antibody therapies, and neurological interventions, no currently available alternative captures the full physiological complexity of a living organism.

There is also a precautionary argument. If an experimental compound proves fatally toxic in an alloprimate study, advancing that compound directly to human trials without that information could be ethically indefensible. From this perspective, alloprimate testing functions as an early warning system when the stakes are highest.

Supporters also point to the role of alloprimates in behavioral and neurological research. Their capacity for complex social behavior, emotional responses, and trainable cognitive tasks makes them attractive for studying psychiatric disorders, addiction, and developmental conditions (Phillips et al., 2014, as cited in Pirzada, 2022).

These arguments deserve consideration. But they ultimately fail to withstand closer scrutiny, particularly when examined alongside the structural incentives that sustain alloprimate research.

Arguments Against Alloprimate Testing

The most fundamental scientific problem is translation. Despite decades of reliance on animal models, approximately 95% of drugs fail in human clinical trials. In Alzheimer’s disease research alone, 99% of clinical trials between 2002 and 2019 failed to produce effective treatments. Similarly, hundreds of HIV vaccines have demonstrated success in animal testing yet failed in human trials. Genetic similarity, it turns out, does not reliably predict human outcomes. The scientific justification for alloprimate testing rests on a premise that the data repeatedly fails to support.

The ethical concerns are equally serious. As Arnason (2020) notes, debates over the use of animals in research increasingly engage the same ethical principles used in human research ethics: autonomy, harms and benefits, justice, and vulnerability. Alloprimates possess high levels of cognitive sophistication, emotional complexity, and social awareness, qualities that demand far more serious ethical consideration than current research practices typically provide.

The realities of laboratory conditions further complicate this picture. Some research facilities continue to fail basic compliance with the Animal Welfare Act, resulting in cases where alloprimate subjects are denied veterinary care or suffer injuries due to inadequate monitoring, improper handling, or poor facility maintenance.

The system also creates ecological consequences. Removal of alloprimates from their native habitats threatens wild populations, while the transportation and confinement of animals can increase the risk of zoonotic disease transmission, a factor that can also compromise the validity of experimental data.

Economically, the system is increasingly difficult to justify. The cost of acquiring and maintaining an alloprimate subject can now reach $50,000 per animal, and long-term animal studies significantly slow the development pipeline for new therapies. Many drugs ultimately fail due to safety issues or lack of efficacy that were not detected in animal tests, meaning that the limitations of alloprimate research often only become clear during human trials.

Even the scientific claims used to justify these studies have come under scrutiny. Researchers and funding institutions have frequently overstated the medical value of alloprimate experiments, and ethics committees have historically approved studies whose scientific contributions later proved minimal.

The Alternatives

A growing suite of human-relevant research technologies is already transforming biomedical science. The question is no longer whether alternatives exist, but whether institutions invested in the current system are willing to allow them to replace alloprimate testing. Three major areas are driving this transition: in vitro culture platforms, advanced disease modeling, and computational simulations. Modern cell culture systems now extend far beyond traditional flat petri dishes. Technologies such as 2D co-culture systems, 3D spheroids, organoids, and organ-on-a-chip platforms allow researchers to recreate complex physiological environments using human cells.

Organoids are miniature, self-organizing tissue structures grown from human stem cells that replicate the architecture and function of real organs, including the brain, liver, intestine, and lungs. Stem cell technologies such as organoids, organ-on-a-chip systems, and induced pluripotent stem cell models address many of the limitations of animal experimentation, including ethical concerns, high costs, and limited translational accuracy. Because these systems are built from human cells, they capture disease processes that alloprimate models frequently fail to replicate.

Organ-on-a-chip systems take this concept further. These microfluidic devices contain living human cells arranged to simulate the mechanical and biochemical environment of functioning organs. Unlike traditional animal models, organ-on-a-chip platforms generate human-relevant pharmacological data on drug toxicity, metabolism, and pharmacokinetics. These technologies are now formally recognized by regulatory agencies. In September 2024, the FDA accepted the first organ-on-a-chip model through its ISTAND regulatory program, signaling that these platforms can move through the same evidentiary pipeline as traditional animal-based testing.

Another rapidly developing field is in silico modeling, which uses artificial intelligence, machine learning, and molecular simulations to predict how drugs behave in the human body before any physical testing occurs.

Some researchers now describe computational modeling as the fourth pillar of biomedical science, alongside theory, experimentation, and observation. AI models can analyze enormous datasets, comparing new drug compounds with hundreds of existing compounds to predict potential toxicity or therapeutic effects, something impossible to accomplish through animal experimentation alone.

Human cell-based assays also allow researchers to test disease responses in tissues derived directly from patients. Immunogenicity testing, toxicity screening, and inflammation modeling can now be partially or fully conducted using human organoid systems combined with computational simulations.

These approaches align with the Replacement, Reduction, and Refinement (3Rs) principles while simultaneously improving the reliability of biomedical research. Regulatory institutions are beginning to respond. In December 2025, the U.S. Senate passed the FDA Modernization Act 3.0, instructing the FDA to expand its process for qualifying new approach methodologies in place of animal testing. Earlier that year, the NIH announced that research proposals relying exclusively on animal data would no longer be eligible for certain forms of agency support. These developments signal a clear shift toward human-based research systems.

Why Full Disengagement Is the Only Ethical Position

The mainstream framing of this debate suggests that ethical reform must wait for scientific consensus. In other words, primate testing can only be abandoned once alternatives are proven to work in every possible research context. This logic has the relationship between science and ethics backwards.

Scientists routinely justify the use of alloprimates by pointing to their biological and behavioral similarities to humans. Yet those same similarities are ignored when ethical protections are considered. We cannot claim that alloprimates are similar enough to humans to serve as valid research subjects while simultaneously insisting that they are different enough to be denied the protections we extend to human participants.That contradiction is not a philosophical puzzle. It is a moral failure. Ethical commitment does not follow scientific readiness. It precedes it. Declaring a practice unacceptable is often what drives the development of alternatives.

History shows this clearly. The end of non-consensual human experimentation, the elimination of pediatric drug trials without consent, and the phaseout of asbestos in construction all followed the same pattern: ethical judgment came first, and technological solutions followed. Waiting for perfect alternatives before committing to ethical change guarantees that the system will never change at all.

Colonial and Capitalist Roots of Alloprimate Testing

The persistence of alloprimate testing in the face of growing scientific and ethical criticism is not accidental. It reflects a global research system built on colonial extraction and economic incentives.

Captive breeding colonies in the United States and Europe cannot meet the demand for research animals, leading to the continued importation of macaques and other alloprimate species from Asia, Africa, and South America. This supply chain follows a familiar pattern: wealthier nations in the Global North extract biological resources from the Global South, generating knowledge and profit that largely flow back to institutions in those same wealthy nations.

Historians of science have described this dynamic as extractive colonialism within biomedical research. Since the mid-twentieth century, the United States and other research powers have relied heavily on primates captured or bred in formerly colonized regions. The pattern continues today. Researchers from high-income countries sometimes conduct experiments in countries where regulations governing alloprimate research are less strict. Tightening ethical standards at home while relocating experimentation abroad does not represent reform. It represents the globalization of harm.

The structure of scientific knowledge production reflects similar inequalities. Most wild alloprimates live in low- and middle-income countries, yet much of the published research about these species is led by scientists from wealthier nations. (the author is guilty of benefiting from her positionality to studty alloprimates across the globe). Local researchers and communities often contribute critical knowledge and labor while receiving limited recognition or authorship.

Meanwhile, the financial infrastructure surrounding alloprimate research continues to expand. Breeding facilities, transport companies, contract research organizations, and pharmaceutical supply chains generate significant revenue. With costs reaching tens of thousands of dollars per animal, the system has powerful economic incentives to sustain itself.

This incentive structure does not mean individual researchers are acting in bad faith. But it does mean that institutions benefiting financially from alloprimate research cannot be treated as neutral voices in debates about whether that system should continue.

Disengaging from alloprimate research is therefore not simply an animal welfare issue. It is also a matter of scientific integrity and global justice.

Ending the use of alloprimates in biomedical research means refusing to continue building the health of wealthy populations on systems that depend on the capture, confinement, and exploitation of animals taken from ecosystems and communities that never consented to their removal.

Citations

Arnason, G. (2020). The emergence and development of animal research ethics: A review with a focus on nonhuman primates. Science and Engineering Ethics, 26(4). https://doi.org/10.1007/s11948-020-00219-z

Bakshi, S., et al. (2025). In silico research is rewriting the rules of drug development: Is it the end of human trials? PMC / PubMed Central. https://pmc.ncbi.nlm.nih.gov/articles/PMC12070237/

Chow, S.K.-H., Shinohara, I., Murayama, M., et al. (2025). Can alternatives to animal testing yield useful information regarding biological mechanisms and drug discovery? Journal of Orthopaedic Translation, 55, 132–145. https://doi.org/10.1016/j.jot.2025.08.005

Druglitrø, T. (2023). Nonhuman primates in public health: Between biological standardization, conservation and care. Journal of the History of Biology. https://doi.org/10.1007/s10739-023-09721-z

Honess, P. (2016). A brief history of primate research: Global health improvements and ethical challenges. Archives of Medical and Biomedical Research, 2(4), 151–157. https://doi.org/10.4314/ambr.v2i4.7

Kumar, P., et al. (2023). Do alternatives to animal experimentation replace preclinical research? Indian Journal of Pharmacology, 55(2), 71–75. https://pmc.ncbi.nlm.nih.gov/articles/PMC10335642/

Lappan, S., Malaivijitnond, S., Radhakrishna, S., et al. (2024). Promoting equitable research partnerships in primatology. International Journal of Primatology. https://doi.org/10.1007/s10764-024-00463-1

Nava, A., et al. (2025). Artificial intelligence-, organoid-, and organ-on-a-chip-powered models to improve pre-clinical animal testing of vaccines and immunotherapeutics. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2025.1681106

Park, G., Rim, Y.A., Sohn, Y., et al. (2024). Replacing animal testing with stem cell-organoids: Advantages and limitations. Stem Cell Reviews and Reports, 20, 1375–1386. https://doi.org/10.1007/s12015-024-10723-5

Phillips, K.A., et al. (2014). Why primate models matter. American Journal of Primatology, 76(9), 801–827. https://doi.org/10.1002/ajp.22281

Pirzada, N. (2022). The ethical dilemma of non-human primate use in biomedical research. Voices in Bioethics, 8. https://doi.org/10.52214/vib.v8i.9348

Prescott, M.J. (2010). Ethics of primate use. Advances in Science and Research, 5(1), 11–22. https://pmc.ncbi.nlm.nih.gov/articles/PMC7610564/

Qin, J., et al. (2025). Synergistic innovation in organ-on-a-chip and organoid technologies. Protein & Cell. https://doi.org/10.1093/procel/pwaf058

Singh, R., et al. (2025). Exploring the potential of computer simulation models in drug testing and biomedical research: A systematic review. Frontiers in Pharmacology. https://doi.org/10.3389/fphar.2025.1644907

Skardal, A., et al. (2024). Grand challenges in organoid and organ-on-a-chip technologies. Frontiers in Bioengineering and Biotechnology, 12. https://doi.org/10.3389/fbioe.2024.1366280

Tardif, S.D., Coleman, K., Hobbs, T.R., & Lutz, C. (2013). IACUC review of nonhuman primate research. ILAR Journal, 54(2), 234–245. https://doi.org/10.1093/ilar/ilt040

Trunnell, E.R., & Mand, D. (2025). Response to "Humanity and Inhumanity of Nonhuman Primate Research." AMA Journal of Ethics, 27(2), E164–166. https://doi.org/10.1001/amajethics.2025.164

Wang, H., et al. (2025). Organoids and organs-on-chips: Recent advances, applications in drug development, and regulatory challenges. Med (Cell Press). https://doi.org/10.1016/j.medj.2025.03.007

Weed, K.R. (2024). Humanity and inhumanity of nonhuman primate research. AMA Journal of Ethics, 26(9), E701–708. https://doi.org/10.1001/amajethics.2024.701

Zhao, Y., Landau, S., Okhovatian, S., et al. (2024). Integrating organoids and organ-on-a-chip devices. Nature Reviews Bioengineering, 2, 588–608. https://doi.org/10.1038/s44222-024-00207-z

Zhao, Y., Wang, E.Y., Lai, F.B.L., Cheung, K., & Radisic, M. (2023). Organs-on-a-chip: A union of tissue engineering and microfabrication. Trends in Biotechnology, 41, 410–424. https://doi.org/10.1016/j.tibtech.2022.12.018

Zhong, X., et al. (2025). From organoids to organoids-on-a-chip: Current applications and challenges in biomedical research. Chinese Medical Journal, 138, 792–807. https://doi.org/10.1097/CM9.0000000000003535

Primary Regulatory and Policy Documents

U.S. Food and Drug Administration. (2025). Roadmap to reducing animal testing in preclinical safety studies. https://www.fda.gov/files/newsroom/published/roadmap_to_reducing_animal_testing_in_preclinical_safety_studies.pdf

FDA Modernization Act 2.0, Pub. L. No. 117-328, § 3209, 136 Stat. 5821 (2022).