Guest speaker: Pedro Mateo Pedro (University of Maryland)

We are delighted to (virtually) welcome Pedro Mateo Pedro, who is the Executive Director of the Guatemala Field Station run by the University of Maryland, and an Assistant Research Professor in the associated Language Science Center. He has a Ph.D. from the University of Kansas (2014) and also teaches at the Universidad del Valle de Guatemala. His research is focused on the Mayan languages, documentation and revitalization, and acquisition and variation. The talk that he will be giving for us, "Language documentation, revitalization, and research in Mayan languages," will be taking place at 3 PM on Friday, March 27, via Zoom meeting; please see the email for a link to the room.

This talk will be divided in three parts. In the first part, I will discuss the documentation projects that I am involved in: acquisition, dialectal variation, and languages in contact. I will highlight the role community members have played in these projects. The second part will be about revitalization projects, emphasizing on teaching method of Mayan languages, workshops in Mayan languages, and creating online materials. The third part will be about my current research projects, in particular my research on the acquisition of numeral classifiers in Q’anjob’al. Q’anjob’al is a language with a classifier system, which includes nominal, numeral, and mensurative. Q’anjob’al uses three numerical classifiers: -eb’, -k’on, and -wan, which are obligatorily suffixed to numbers to classify objects, animals, and people. The data come from three children: Xhuw (1;9-3;0), Xhim (2;3-4;0), Tum (2;7-3;6). This is the first study on the acquisition of numeral classifiers in Q’anjob’al. The study shows that children acquire numeral classifiers around the age of two. However, these children show extension errors; errors that are also seen in 10-year-old children. The data suggest that children seem to consider the numeral classifier -eb’ as a default form, especially Xhuw. The semantic complexity of what is being classified (objects, animals, or people) and the variation of use of these classifiers in the input may contribute to the extension errors. Errors of this type has also been reported in the acquisition of numeral classifiers in Yucatec (Pfeiler 2009) and Malay (Salehuddin 2009).