Nicolas Rufino dos Santos
An epistemic framework is the worldview that expresses a researcher’s values and intentions, even if implicitly. It is through this analytical dimension that one can extract the ideological roots of scientific theories. The selected scopes and the interpretations drawn from them reveal the epistemic framework (GARCÍA, 1994).
To illustrate the importance of the epistemic framework, let us use an example presented by García (1994). Imagine two research plans on the same theme. A project designed to answer the question, “How can we proceed to increase the productivity of staple foods to achieve food self-sufficiency?” will be epistemologically distinct from a project aimed at asking, “Why is malnutrition increasing among poor countries?” Even if both projects raise problems related to productivity and nutrition, they stem from different normative and social perspectives.
It is for this reason that García (1994) argues that no entirely neutral observable phenomena exist. The data collected through the researcher’s experience becomes intelligible throughout the research process. There is no observation without interpretation. Any and all empirical data is collected by someone who already carries a way of seeing the world, and it is this lens that makes the data recognizable. The epistemic framework conditions what the researcher sees, what they ask, and how they interpret.
Unfortunately, the current system of education and scientific production is characterized by the fragmentation of real-world problems and, consequently, a loss of contact with them. An example of this phenomenon is traditional medicine, where, by isolating the study of specific organs, one risks losing sight of the organism’s functioning as a whole. To overcome this problem, it is not necessarily a matter of absorbing more content, but of thinking differently about scientific practice (GARCÍA, 1994).
When we study Systems Theory, for example, we notice that the delimitation of a system is not a “given” of nature, but a theoretical construction guided by the inquiries of the individual investigating it. Even so, the relations between its elements impose methodological requirements for its study. The “rule” to be followed is: a complex system is not characterized solely by being “complicated” or by gathering heterogeneous parts, but by requiring shared epistemic, conceptual, and methodological frameworks. This is because its determining characteristics are inter-definability and the functional dependence between its elements (GARCÍA, 1994).
Based on this premise, García (1994) warns us of a methodological trap in modern science: the illusion of multidisciplinary teams. It is a common belief that simply gathering specialists from different fields in the same room is enough to solve a complex problem. The issue is this: the mere juxtaposition of professionals does not produce interdisciplinarity. Groups created this way produce a pile of isolated, specific reports published under a single cover. There is no integrative synthesis.
Interdisciplinarity happens at the starting point—in the joint construction of the epistemic framework—and not in the cross-referencing of data at the final stage of a project. It is not found at the boundaries of academic disciplines, but in the processes of the system to be studied and in the epistemic framework that guides the initial formulation of problems (GARCÍA, 1994).
For example: a mayor hired three specialists to implement an Artificial Intelligence (AI) system to organize the surgical waiting list in a public health department: an engineer, a lawyer, and a doctor. It is common for the engineer to create an algorithm focused on efficiency and speed; the lawyer to write an opinion stating the city is complying with data protection laws (LGPD); and the doctor to write a report on the city’s most common diseases. The final result is nothing more than a series of isolated reports held together by a single staple.
True interdisciplinarity happens when they sit together in a room to construct the problem, debating the intentions and values of the project to reach an agreement, such as: “The problem is not just making the line move faster, but ensuring that the AI promotes equity and fair access to healthcare for the most vulnerable.” From this point of view, the engineer will work with the doctor to create code that meets criteria for social vulnerability and medical severity. The lawyer, instead of applying the LGPD in isolation, will talk to the engineer to ensure the algorithm does not violate constitutional principles of non-discrimination. The doctor will not merely list the city’s most common diseases but will engage with the engineer to define which clinical criteria must be translated into algorithmic variables. And the mayor is the one who defines which problem the team is authorized to solve.
A shared point of view is what transforms isolated specialists into a true team; the epistemic framework is the condition of possibility for the project, not a final stage. A team oriented by efficiency will solve only that, regardless of how many disciplines it brings together.
References:
GARCÍA, Rolando et al. Interdisciplinariedad y sistemas complejos. Ciencias sociales y formación ambiental, p. 85-124, 1994.
GARCÍA, Rolando. Conceptos Básicos para el Estudio de Sistemas Complejos. In: LEFF, E. (Coord.). Los Problemas del Conocimiento y la Perspectiva Ambiental del Desarrollo. México: Siglo XXI, 1986.
