The Myth of the Self-Made Scientist

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When a scientist wins a major award, we instinctively ask what makes that person exceptional. We look for intelligence, determination, creativity, or grit. Rarely do we ask about their collaborators, mentors, or professional network. This tendency reflects a broader inclination in human psychology: We are remarkably good at seeing individuals and remarkably poor at seeing systems.

We tend to talk about success as if it belongs to individuals: A scientist is “brilliant”. A politician is “gifted”. A student is “honours-material”.

The underlying assumption is straightforward: Achievement reflects personal qualities like intelligence, effort, motivation, or persistence. But this story is incomplete. Every success is supported by something much less visible yet quietly powerful: the structure of the networks in which that person is embedded.

The invisible scaffolding of achievement

In science, politics, and school, success is often treated as simply the output of individual merit. Yet research on social networks suggests something different: Outcomes are shaped not just by who people are, but by where they are embedded:

• Who they collaborate with.
• Who notices their work.
• Who boosts their visibility.
• Who invites them into conversations and opportunities.

These are not random processes. Consider mentorship. In a recent preprint by Martínez-Goñi and colleagues, graduate students identified empathy and emotional support as key qualities they seek in supervisors. This finding challenges the image of science as a purely intellectual enterprise. Relationships matter, and supportive mentors can open opportunities, foster confidence, and help researchers navigate setbacks.

Small differences in one’s network—for example, early mentorship ties—can accumulate into large differences in visibility and recognition over time. In other words, achievement is scaffolded. (These dynamics are explored in more detail in my recent preprint (Vromen, 2026), in which I look at how researchers’ early collaboration networks shape who stays active in science publishing and how prolific they become.)

When visibility becomes mistaken for value

One of the most important distortions in systems like academia is the assumption that visibility equals quality:

• A well-connected idea spreads faster.
• A well-placed researcher is more likely to be cited.
• A well-networked lab is more likely to be funded.

Over time, this creates a feedback loop: Visibility increases opportunity, and opportunity increases visibility. What looks like individual excellence may, in part, reflect accumulated advantage within a system that amplifies certain scientists more than others. Ability is only one ingredient in a much larger system.

The Matthew Effect: How advantage compounds

Sociologists have long described a pattern known as the “Matthew Effect”, which is the idea that advantage tends to accumulate over time:

• Those who are already visible tend to become more visible.
• Those who are already cited tend to be cited more.
• Those who are already connected tend to gain more connections.

In science, this can be subtle but powerful. Early career opportunities, institutional placement, mentorship, and collaboration networks can shape trajectories long before output is ever evaluated. The result is that success is often path dependent. Small early differences can grow into large later disparities. In the same way, a plane being a single, small degree off course when taking off from LAX may end up in Washington DC instead of its planned destination of New York.

How we judge people

If we see success as purely individual, we risk over-attributing success to personal brilliance, underattributing it to structural positioning, and misinterpreting inequality as difference in talent rather than difference in access.

This can shape hiring, funding decisions, and even informal judgments about who is “promising” or “capable”. It can also shape how individuals see themselves, either as uniquely deserving of success or, conversely, as failing to measure up in systems they do not fully see.

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A more accurate way to think about achievement

As I argue in my preprint, a systems view does not remove individual agency. Instead, it reframes it. Individuals are not isolated units producing outcomes in a vacuum. They are nodes within dynamic systems of interaction, reinforcement, and visibility. Instead of only asking, “Who is most talented?” we might also ask, “Who is well positioned to be seen?” “Which networks amplify which kinds of contributions?” and “What forms of work remain structurally invisible?”

This is not just an academic issue

Although this discussion is grounded in science and academia, the implications extend far beyond it. Many domains including workplaces, leadership pipelines, creative industries, and healthcare systems operate through similar network dynamics. Network dynamics also influence gender and racial disparities:

• Who gets noticed is not always the same as who contributes most.
• Who advances is not always the same as who works hardest.
• Who is recognised is not always the same as who is most skilled.

Understanding this can help us clarify achievement.

Rethinking success

A more honest model of success requires consideration of not only individual or systemic influences, but an interaction between the two. People bring effort, creativity, intelligence, and persistence. Systems shape which of these qualities are seen, rewarded, nurtured and sustained. When we ignore the system, we over-personalise outcomes. When we ignore the individual, we miss agency. But when we hold both together, we get closer to how achievement actually is created.

Final reflection

It can be uncomfortable to realise that success is not purely earned in isolation. But it is also liberating. Because if achievement is shaped by networks, then it is not fixed. Networks can be brought into awareness and slowly changed. Visibility can be redistributed. Opportunities can be redesigned. And that means success is not just a reflection of who people are but a reflection of the systems we build and the systems we are willing to see.