This paper addresses two problems: Firstly, the problem of classifying remote and collocated small-group working meet- ings, and secondly, the problem of identifying the remote participant, using in both cases nonverbal behavioral cues. Such classifiers can be used to improve the design of remote collaboration technologies to make remote interactions as ef- fective as possible to collocated interactions. We hypothesize that the difference in the dynamics between collocated and remote meetings is significant and measurable using speech activity based nonverbal cues. Our results on a publicly available dataset - the Augmented Multi-Party Interaction with Distance Access (AMIDA) corpus - show that such an approach is promising, although more controlled settings and more data are needed to explore the addressed prob- lems further.