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Thick Evaluative Concepts: Value, Reasons, and the Natural World

Description

Developments in linguistics and metaphysics open unprecedented opportunities for advances using up-to-date methods from these areas in metaethics: an area of moral philosophy that studies these topics as they arise with respect to normative issues. The proposed research is a full-scale study of “thick” evaluative concepts, led by the coordinator of this proposal and building on his US work with such methods in metaethical study of general moral principles and their role in moral knowledge and guidance. The goal of the proposed work is to enable the coordinator to transition his methods to new work on thick value concepts and establish himself in European context. The project is unique in using up-to-date work in linguistics on gradable adjectives to analyse the meaning of evaluative language, focusing on concepts that are “thick” in that they combine evaluation with robust natural non-evaluative conditions on their applicability (e.g. 'cruel', 'honest'). It will determine whether these elements of thick concepts decompose and so whether there is any basis for a robust fact-value distinction. It will investigate whether thick value judgements express beliefs that can be true or false or non-cognitive sentiments that cannot, and whether thick concepts correspond to real properties in the natural world. The project's innovative methodology, and the new perspectives and results it enables, will have a major impact on the state of the art. The project is timely and humanly relevant, especially given the role thick concepts play in moral disagreements that reflect cultural and social differences. The proposed project enables the reintegration of a world-class, experienced European researcher. It is designed to stimulate interaction and collaboration among US and European researchers, and is fully aligned with the coordinator's forthcoming senior, long-term role at a major UK host organisation, the University of Leeds, so as to secure a strong knowledge transfer path.

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