In the 1990s, researchers finally began studying heart disease in women—decades after it had become the leading cause of female death in the United States. For years, major clinical trials had enrolled only male subjects, and doctors had been trained to recognize only male symptom patterns. Women were dying of heart attacks that looked nothing like the textbook descriptions, because the textbooks had been written around men's bodies.

This wasn't a conspiracy. It was an epistemological blind spot—a gap in knowledge created not by bad science, but by who was doing the science and what questions they thought to ask. Feminist philosophers of science argue that cases like this reveal something important about how social position shapes scientific knowledge, and that addressing these biases doesn't weaken objectivity but strengthens it.

Standpoint epistemology, developed by philosophers like Sandra Harding and sociologists like Dorothy Smith, makes a simple but powerful claim: your social position affects what you notice, what puzzles you, and what you take for granted. A factory worker sees the production line differently than the CEO does—not because one is smarter, but because they occupy different positions within the same system. Both perspectives are partial, but they illuminate different features of reality.

Applied to science, this means that the demographics of a research community shape its collective vision. When primatology was dominated by male researchers, studies of primate behavior focused overwhelmingly on competition, dominance hierarchies, and male aggression. When women like Jane Goodall and Sarah Hrdy entered the field, they began documenting female choice, cooperative behavior, and complex social strategies that had been there all along but hadn't been the focus of inquiry.

This doesn't mean that knowledge is merely subjective or that anyone's personal feelings count as evidence. Standpoint epistemology is an argument about starting points—about which questions get asked, which phenomena get noticed, and which assumptions go unexamined. The data still has to hold up to scrutiny. But what counts as interesting data, and what frameworks we use to interpret it, are shaped by the lived experiences of the people doing the research.

Takeaway

Objectivity isn't a view from nowhere—it's achieved by recognizing that every observer has a somewhere, and that different somewheres reveal different features of reality.

Consider a thought experiment. If the entire medical research community were composed of people who menstruate, how long would it have taken to develop effective treatments for endometriosis? Probably not the average of seven to ten years that patients currently wait for a diagnosis. The questions a scientific community pursues aren't chosen by pure logic—they emerge from what the community finds puzzling, urgent, or fundable. And those judgments are shaped by who sits in the room.

This extends well beyond medicine. Agricultural research has historically prioritized cash crops managed by men over subsistence crops managed by women, even in regions where women produce the majority of food. Artificial intelligence systems trained on data reflecting existing social patterns have reproduced and amplified gender and racial biases. In each case, the issue isn't that individual scientists are sexist—it's that homogeneous communities have homogeneous blind spots.

The philosopher Helen Longino argues that scientific knowledge is fundamentally social—it's produced through communities of inquiry, not isolated minds. The norms of those communities, including who gets to participate and whose concerns are taken seriously, function as a kind of filter on what knowledge gets produced. Diversifying scientific communities doesn't just serve social justice. It serves epistemic justice—it expands the range of questions science can ask and the phenomena it can detect.

Takeaway

Science doesn't just answer questions—it chooses which questions to ask. And that choice is never purely scientific. It reflects the interests, experiences, and blind spots of the people making it.

A common worry about the feminist critique is that it undermines objectivity—that once you admit social factors shape science, you've opened the door to relativism where anyone's opinion is as good as anyone else's. Sandra Harding's concept of strong objectivity pushes back against this directly. She argues that conventional objectivity is actually too weak, because it only requires that scientists follow established methods without ever examining whether those methods have built-in biases. Strong objectivity demands that scientists also scrutinize the social conditions under which knowledge is produced.

Think of it this way. A telescope with a smudge on one side of the lens will produce images that pass internal consistency checks—everything will look sharp and coherent within the visible field. But you're systematically missing part of the sky. Conventional objectivity checks the sharpness of the image. Strong objectivity also asks whether you've cleaned the whole lens.

This is why including marginalized perspectives isn't about lowering standards or replacing evidence with ideology. It's about raising standards. When people with different life experiences examine the same evidence, they're more likely to spot assumptions that a homogeneous group would treat as obvious truths. The result isn't less rigorous science—it's science that has been subjected to a wider and more demanding range of critical scrutiny.

Takeaway

True objectivity doesn't come from pretending we have no perspective. It comes from systematically including enough different perspectives that hidden assumptions get exposed rather than reinforced.

The feminist critique of science isn't an attack on the scientific enterprise. It's a call to take its own ideals more seriously. If science aims at objective knowledge of the natural world, then it should care deeply about the social conditions that make objectivity possible—including who participates in the process of inquiry.

The heart disease case we began with wasn't solved by abandoning evidence-based medicine. It was solved by expanding who counted as a subject, who asked the questions, and who noticed the gaps. That's not ideology. That's better science.