The main focus of the project was a cross-linguistic and cross-theoretical analysis of whole-entity classifiers in sign languages. When describing motion events, many sign languages use complex signs in which every component of the sign (its handshape, location, and movement) is meaningful (Zwitserlood 2012). The shape of the sign's movement and its direction in space depict the motion of the moving object. Importantly, the sign's handshape indicates that the moving object belongs to a certain semantic class, for example, that it is a person. If a different handshape is used in the same sign, the meaning would change. Sign languages have limited numbers of such handshapes referring to different semantic classes, such as humans, vehicles, round objects, small objects, etc. Commonly, although not by all researchers, handshapes in such signs are called "classifiers", and these signs are called "classifier predicates" (Schembri 2003). There are different types of classifiers, but this project focused on whole-entity classifiers, also known as semantic classifiers, when the handshape in the sign refers to the moving object as a whole, and not to another participant manipulating the object.