at certain degrees and levels of effectivity. As previously discussed, scientific practice produces general objective true knowledge about abstract objects, which very usually contradicts the commonsense ideas that are usually related to immediate representations arising from other social practices of our daily lives. This clearly leads to obstacles to scientists, who are both agents of the scientific practice and individuals living in society. The totalizing tendency of philosophy also plays a role: it either distorts scientific theories and concepts to fit in universal systems of philosophical categories or judges their truth value based on universal methodological assumptions. This directly or indirectly affects the self‐understanding of the relation that the scientists have with their own practice, creating new obstacles to the science development [9, 10].
Example 1.2 Differences between daily language, philosophical categories, and scientific concepts. One word that exemplifies very well the difference is time. We use word time in many ways in our daily lives: to discuss about our activities, plans, routine, and the like. However, in philosophy, the category Time has different roles depending on the philosophical system to be considered – this usually comes with the relation between other categories like Causality, Origin, and End. In sciences, time is also a concept in different disciplines. In physics, time is a very precise concept that has been changing throughout its history, changing (not without pain) from the classical definition that time is an absolute measure (i.e. the same everywhere) to today's relativity theory where time is relative (and the speed of light is absolute). Such a scientific definition of the concept of time is not intuitive at all, and goes against most of our immediate use of the word. In this sense, scientists may find it difficult to operate with the scientific concept of time in relativity theory because of the other more usual meanings of the word. Besides, such a confusion between the scientific and the nonscientific may open philosophical questions and nonscientific interpretations of the scientific results.
In addition to this unavoidable challenge, the rationalization required by scientific theories appears in different forms. In this case, philosophical practice can help scientific practice by classifying the different types of rationality depending on the object under consideration. Motivated by Lepskiy [11] (but understood here in a different manner) and Althusser [8], we propose the following division.
Classical scientific rationality: Direct observations and empirical falsification are possible for all elements of the theory, i.e. there is a one‐to‐one map between the physical and abstract realities.
Nonclassical scientific rationality: Observations are not directly possible, i.e. the process of abstraction leads to nonobservable steps, resulting in a relatively autonomous theoretical domain.
Interventionist scientific rationality: Active elements with internal awareness with objectives and goals exist, leading to a theory of the fact to be accomplished in contrast to theories of the accomplished facts.
By acknowledging the differences between these forms of rationality, sciences and scientific knowledge can be internalized as a social practice within the existing mode of production. Different from positivist and existentialist traditions in philosophy, this practice of philosophy attempts to articulate the scientific practice within the historical social whole, critically building demarcations of the correctness of the reach of scientific knowledge by rational argumentation [10].
Example 1.3 Scientific efforts related to COVID‐19. In 2020, an unprecedented channeling of research activities was directed to combat the COVID‐19 pandemics. These activities can be classified following the three aforementioned scientific rationalities. The classical rationality can be exemplified by the process to test the effectiveness of vaccines following the historically defined norms. The nonclassical rationality incorporates the mathematical models for epidemics based on nonlinear dynamical processes over graphs where not all variables are observable. The interventionist rationality considers lockdown policies to control the virus propagation as a fact to be accomplished. A critical philosophical practice demarcates the reach of the three different scientific activities, determining both their interrelations and the articulation with other social practices. For example, a vaccine that is proved to work can be modeled by a mathematical model, which can be used to change the lockdown policy. However, within the capitalist mode of production, these activities are directly or indirectly determined by the economical reality – from the funds available to develop the vaccine and its respective property rights to the economic impact of lockdown policies and its justifications based on a wide range of epidemiological models. A critical philosophical practice acknowledges the autonomy of the results obtained through the scientific practice with respect to its object while it internalizes such a practice in the articulated social whole.
This philosophical practice goes hand in hand with the scientific practice by helping scientists to avoid overreaching tendencies related to their own theoretical findings. It also indicates critical points where other practices might be interfering in the scientific activity and vice versa. Although a deep discussion of the complex relations between scientific practice and other practices are far beyond our aim here, we will throughout this book deal with one specific relation: how scientific practice is related to the technological development. We have seen so far that the practical development of techniques does not require the intervention of (abstract) scientific rationalization. On the other hand, the knowledge produced by the sciences has a lot to offer to practical techniques. The existence of the term technology, referring to techniques developed or rectified by the sciences, indicates such a relation. More than what this definition might suggest, technology cannot be simply reduced to a mere application of scientific knowledge; it can indeed create new domains and objects subject to a new scientific discourse.
The aforementioned control and information theories perfectly exemplify this. New technological artifacts had been constructed using the up‐to‐date knowledge of physical laws to solve specific concrete problems, almost in a trial‐and‐error basis to create know‐how‐type of knowledge pushed by the needs of the industrial revolution. At some point, these concrete artifacts were conceptualized as abstract objects toward a scientific theory with its own methods, proofs, and research questions, constituting a relatively autonomous science of specific technological objects. The new established science not only indicates how to improve the efficiency of existing techniques and/or artifacts but also (and very importantly) defines their fundamental characteristics, conditions, and limits.
Example 1.4 Information theory. Although large‐scale communications systems had already been deployed for some decades before the 1940s, the engineers considered that errors in transmission were somehow inevitable. This commonsense practical knowledge was scientifically proven false when Claude Shannon published A Mathematical Theory of Communication [12] formulating the concept of information entropy and mutual information. Using these concepts, Shannon mathematically proved the existence of a code that leads to error‐free communication if, and only if, the coding rate is below the channel capacity. This theory proposed in 1948 opened up a new field of theoretical research and also oriented practical deployments by giving an absolute indication of how far from the fundamental limit specific technologies are. It is noteworthy that, although Shannon had mathematically proven the existence of capacity‐achieving codes, he has not indicated how to practically design them. For many years, researchers and engineers have pushed the technological boundaries and have developed different coding schemes. Only with the new millennium, feasible solutions have been proposed (or rediscovered) and, currently, the turbo codes and low‐density parity‐check (LDPC) codes are feasible options to reach a performance close to Shannon's limit. These high‐performance techniques are used for example in cellular networks and satellite communications. The fundamental limit proposed by Shannon, though, cannot be surpassed by any existing or future technologies. A similar development happened in physics when the fundamental
