“Whereas the morula comprises a few dozen cells, the blastocyst will come to encompass hundreds of cells.”
When the common reader encounters a sentence like the one above, we are likely to shut down. The terms of biological science are so alien to the untrained eye that they might as well be incantations. As a society, we grant those who can recite these complex propositions the deference we would a magic-wielder, and we hesitate to jump in the conversation or criticize. Perhaps it’s because we misguidedly accept their mastery of reality and truth; perhaps it is simply because the unwieldy jargon serves as a barrier to entry.
Social sciences are easier to discuss. Popular culture is flooded with criticism of the field, and the layperson doesn’t think twice before offering their humble opinion on a given social subject. But why, unlike with “hard” sciences, do we feel comfortable criticizing these theories? Perhaps because social sciences function by boiling complex phenomena down to a few understandable variables through a process called “modelling.”
When a social science model is built, the creator has to simplify. A basic voter model in political science might start with the few variables a political scientist believes to be most relevant, like age and location. Anyone can understand these models, reflect on their own lived experience, and criticize these theories for what they lack or misrepresent. “What about race? Or gender?” one might ask. The good political scientist would then add more variables into their model, making it increasingly specific. Average citizens do not need to understand fancy terminology like “median voter theorem” to participate in the conversation. Throughout this whole process, it becomes blatantly obvious that the model is simply constructed.
As we develop the specificity and consistency of social models, we notice that they are merely invented to describe how our social world functions. Economists, sociologists, and political scientists often receive flack for creating theories rather than discovering what objective phenomena form our world. The same is not said of “hard” sciences. Concepts in biology, chemistry, and physics are viewed as descriptions of reality in an objective sense. Their “laws” might as well be immutable, and their word is truth. But aren’t these sciences all made up of models in the same sense as psychology, anthropology, and linguistics?
A physicist seeks to describe the nature of reality using the vocabulary of abstract mathematics. They observe patterns in the world, and write down equations to describe those patterns. Take dark matter: a keen physicist observes that the universe is expanding at a growing rate, and cannot describe this phenomenon with the available tools. Thus, the scientist would add a variable into their inter-galactic force interaction models to include acceleration of these bodies. This process is quite similar to that of a social scientist: observe patterns, create models, observe patterns that don’t conform, add or change elements in the model so it better fits the perception of reality.
If their process is so similar to the social sciences, why do we hesitate to criticize “hard” scientific ideas? Often, complex concepts can be intuited by many people—they aren’t restricted to the scientist. The chemist may understand the covalent bonds of H2O, but it doesn’t take a genius to see that water boils and ice melts at relatively close temperatures compared to, say, alcohol. Most people have a good sense of the concepts within the “hard” sciences, especially if they are given the proper tools to observe them, like telescopes or test tubes. The barrier to entry in conversation is often complicated vocabulary for specific ideas. My advice to the reader: don’t be scared off by words like “pneumonoultramicroscopicsilicovolcanoconiosis.” They’re often less complicated than you might think. Models of all types rely on criticism from as many people as possible; every experience is important. Without criticism of modern ideas, our scientific understanding would never develop. Questioning our models is the only way we can improve them.