Although the fuzzy approach has been so far applied mainly in engineering and other technical areas its domain may lie (also) elsewhere, namely, in areas based on human-defined social concepts. The reason comes from the fuzziness as the model of vagueness, which is an omnipresent feature of most social concepts defined by humans while in technologically oriented areas, fuzziness was rather used as a model of gradedness that helped to model properties changing continuously on their domains with some inherent imprecision in sensoring these properties. As the quality of sensoring and measuring devices increased substantially in the last decades and distinct data-driven black-box models as well, this traditional "help" of fuzzy models seems less and less important. This paradoxically helps fuzzy methods to be driven to areas less technologically oriented where: 
1. data is not (and even cannot be) at disposal in a sufficient largeness to train data-driven models, 
2. the goals are only vaguely defined with no way how to redefine them objectively and in a measurable way, 
3. some sort of expert knowledge that is more or less generally accepted by the (expert) community is at disposal, 
4. exists an increasing trend in using innovative computational approaches to support the research. 
These less technologically oriented areas are mainly from social sciences. This abstract describes one of such seminal co-operations in which our Institute took part in a three-year-long TACR project under the wings of the Faculty of Social Sciences. The output of the project was an SW package named "EVKA" that serves for the responsive evaluation of community work and employs distinct expert system modules guiding community workers.