Language of Innovative Agonology: a Guide in Combining Micro and Macro Scales of Preventive, Therapeutic and Defensive Actions

The language of innovative agonology (IA) concerns three layers. One is the terminology, whose basic frame of reference is Tadeusz Kotarbiński’s general theory of struggle (agonology). The remaining ones are: the ambiguity of the same terms used in different sciences (some of which have been appropriated by the public media language); and the need to adapt key words (and even to create neologisms in English for the universal language of IA) which describes scientific discoveries of phenomena included the field of IA research, but originally published in languages other than English.Kotarbiński’s definition of struggle relates to people: “a struggle is any activity, in which at least two subjects participate (assuming that a team can be a subject), while at least one of the subjects hinders the other”. However, Jaroslaw Rudniański’s extension of the concept of ‘struggle’ means that in a certain category of combat (the fight against the pandemic), the parties may not only be human beings. Therefore, in the language of IA (that is, also in a methodology based on a complementary approach), it is legitimate to assume that the ‘material’ means the opponent (subject or thing) with whom the person (individually or in a group) fights.An example of the dilemmas with the ambiguity of using the same terms in different areas of communication is the semantic category ‘development’, elementary to IA (a term used only in a positive sense). The commonly accepted phrase ‘development of a disease’ is patently absurd. Even if the user explains that they meant ‘development in a negative sense’, all the more it shows that they are ignorant as to the semantics.One of the concepts originating from the Lviv-Warsaw school of the methodology of sciences is the term ‘wiedzotwórczy’, which has no equivalent in English. In many methodological arguments edited in Polish, it is a convenient combination of the words ‘knowledge’ and ‘creative’. Future IA professionals will undoubtedly face similar semantic dilemmas. Identifying, interpreting and formulating algorithms for phenomena that need to be effectively addressed (from micro to macro scales) in the areas of prevention, therapy and, in certain circumstances, call for undertaking a strong defense, requires unambiguous language that is understandable to experts in even distant fields.