This work presents a mathematically proven approach to research originating in the field of science studies. It is an attempt that elaborates philosophical values to affect scientific research and technological innovation. This work can more technically be described as a "Classification-Transformation" Method (CTM). This means that it classifies experiences, qualities, properties and characteristics of the inputs, processes and outcomes, relating with a physic-chemical phenomenon taking place in a specific system and mathematically transforms them through complexity levels. The aim of this work was to tackle the objects of hypothetically questioned events, as part of words of increased complexity, in order to reveal the cohesiveness among the complexity levels, as well as the similarity identified vie the interconnection of these levels and the potential transition between them. The phenomenological expressions of the systemic participants can be strongly associated with forceful fields of classification and their descriptors. Accordingly, the goal is to critically describe the evolution patterns of the existing phenomena via their expressions under certain conditions. Our method assumes that all relations in a world, are inherently existing, although not clearly revealed, therefore they remain misperceived, unexplored or unknown. In that sense, a critical, yet predictive, capability is developed via mathematically imposed transition rules among the complexity levels, allowing for the identification of internal similarities among the different complexity levels of the word and pointing towards the research gaps much in need for completing the classes, in a cyclic process of understanding. Conclusively, this work is a constructivist approach in that tries to avoid non-essentialist explanations of events, research repetition or missing of particular research challenges and potential innovations. The ultimate target is to explain a successful theory by understanding the combinations and interactions of elements under well-defined conditions that make it effective and efficient, rather than recording the “true” and “false” perceptions of the events. To conclude with, our approach describes and tries to explain the world by focusing in the cohesions among the principal system descriptors, rather than in their description itself.