What kind of scientist are you?

Identifying your own archetype with this simple tool can help your work.

13 November 2019

Chris Madden/Getty

As researchers, it’s important to be aware of our own strengths, as well as our limitations and what motivates us. But not only is this kind of self-reflection often difficult, it can feel nebulous and imprecise.

To address this challenge, we introduce a new typology that identifies different types of scientists, irrespective of their field of research.

Instead of presuming scientists to be a product of their disciplinary community, we argue that it’s possible to identify researcher types based on their approach to scientific investigation.

Identifying your own archetype can provide several benefits, including a basis for more objective self-analysis and reflection, and a better understanding of your colleagues and how they are likely to complement your own strengths and limitations.

What’s your type?

We base our approach on two dimensions that have proven to be critical for scientific advances.

The first deals with the epistemology of the researcher. That is, do they prefer to conduct their research a priori or a posteriori? In simple terms, a posteriori refers to knowledge based on facts derived from observational or societal experience. A priori describes knowledge that comes from theoretical reasoning, prior to observation or personal experience.

While some disciplines tend to lock a researcher into one or the other, such as mathematics and philosophy, which often prefer a priori conceptualization, many allow for both approaches.

The second dimension of our tool looks at whether a scientist conducts research that is within the paradigm of their field, or outside, in terms of assumptions, theories, methods and contributions.

Combining these two dimensions, we obtain a 2x2 grid with four cells, each representing a very different type of study that would appeal to a specific type of researcher.

Bohr vs Fleming vs Wiles

This typology can be illustrated with some well-known examples.

Here, an example of an a priori study that is conducted within an existing paradigm can be illustrated by Andrew Wiles, a British mathematician best known for proving Fermat’s last theorem.

Wiles solved the famous problem, Fermat’s last theorem, which had stumped mathematicians for 350 years. It was an established problem (existing paradigm) that Wiles had pondered for several years (a priori), and solving it won him the 2016 Abel Prize.

The strength of this type of scientist is that they are able to work on established problems in the field, but the limitation can be that they sometimes prefer to work individually rather than collaboratively.

A different approach is illustrated by Alexander Fleming, who famously discovered penicillin by accident.

Fleming had placed an uncovered petri dish near a window, which became contaminated with mould. Instead of merely throwing out the dish, he realized that bacteria near the mold were dying, which resulted in the identification of Penicillin.

Fleming’s discovery required him to move outside his paradigm when faced with the petri dish (a posteriori). The strength of this type of scientist is that they can make new, data-driven discoveries, but they risk becoming distracted by many different observations at once.

If we look within the field of physics, we can illustrate the two remaining archetypes.

Niels Bohr came up with the Bohr model through a conceptual approach that was a priori, but outside the existing paradigm. The Bohr model of the atom was the first that incorporated quantum theory, and it therefore departed radically from previous descriptions of the atom.

The strength of this type of scientist is that they can create entirely new paradigms, but the limitation can be that they risk becoming too isolated from the mainstream.

More recently, technological advancements have allowed physicists to run real-world experiments on previous conceptual work, as illustrated by the discovery of the Higgs boson particle by CERN scientists, which was a posteriori and inside the existing paradigm.

The strength of this type of scientist is that they can use data to build cumulative knowledge in the field, but the limitation can be that they might be reluctant to look outside their field to challenge underlying assumptions.

Although Wiles and the CERN researchers worked on problems that were typical of their fields, both Bohr and Fleming worked ‘outside the box’ in how they approached and reframed their objects of study.

As much as serendipity is acknowledged to advance scientific breakthroughs, it could be argued that the ability to work in an unconventional way like Fleming did is harder today than it was more than a century ago due to current publication pressures, research requirements and diverse deadlines.

How to use the matrix

As the above examples illustrate, all four types of researchers are essential for knowledge creation. That said, certain fields such as logic and philosophy, by their very nature, may give more weight to certain types of individuals.

As well as being helpful for individual researchers, our simple matrix can provide an overview of research types that dominate specific disciplines or universities, allowing for the mapping of existing groups of researchers and potential blind spots in the communities.

The typology can be extended to provide an overview – and help actively manage – funding for research.

Funding activities could be mapped in relation to how it is being allocated, for example, funding bodies may give more weight to certain types of individuals or different kinds of research.

Finally, our classification matrix can be used to spur communication and collaboration between different disciplines, providing a valuable avenue for interdisciplinary research.

As the matrix provides an overview of different types of scientists who all have varying approaches to research, it can be used as a tool to understand how different scientists work, and whether or not a single approach dominates a given field. Such an understanding makes it possible for scientists of different types to better comprehend, communicate and collaborate.

Carsten Lund Pedersen is an Assistant Professor in the Department of Marketing and Thomas Ritter is a Professor in the Department of Strategy and Innovation, both at the Copenhagen Business School.