In cultural heritage objects, digitized or not, content indicators occurring on higher than word level are often called motifs or their equivalent. Their recognition for document classification and retrieval is largely unresolved. Work on identifying rhetorical, narrative and persuasive elements in scientific texts has been progressing, in several, but largely unconnected tracks. The AMICUS project1 (running between 2009 and 2012) set out to test a possible way to resolve these issues, starting with the identification of Proppian functions in folk tale corpora and adapting the solution to the identification of tale motifs or their functional counterparts. AMICUS has devoted its first project year to listing the corpora, tools, methods and contacts available to address these issues. The initiators of the project have identified a common need in the processing of texts from both the cultural heritage (CH) and scientific communication (SC) domains: to perform automated, large-scale higher-order text analytics, i.e., to reach an advanced level of text understanding so that structured knowledge can be extracted from unstructured text. The four research groups propose to tackle an important aspect of this complex issue by investigating how linguistic elements convey motifs in texts from the CH and the SC domains. Our shared working hypothesis is that the identity of higherorder content-bearing elements, i.e., textual units that are typically designated for e.g. document indexing, classification, enrichment, and the like, strongly depends on community perception.