Metaphors abound in descriptions of the COVID-19 pandemic: it is described, among other things, as a war, a flood, and a marathon. However, not all metaphors may resonate equally well with members...

Concreteness ratings are frequently used in a variety of disciplines to operationalize differences between concrete and abstract words and concepts. However, most ratings studies present items in isolation, thereby overlooking the potential polysemy of words. Consequently, ratings for polysemous words may be conflated, causing a threat to the validity of concreteness‐ratings studies. This is particularly relevant to metaphorical words, which typically describe something abstract in terms of something more concrete. To investigate whether perceived concreteness ratings differ for metaphorical versus non‐metaphorical (...) word meanings, we obtained concreteness ratings for 96 English nouns from 230 participants. Results show that nouns are perceived as less concrete when a metaphorical (versus non‐metaphorical) meaning is triggered. We thus recommend taking metaphoricity into account in future concreteness‐ratings studies to further improve the quality and reliability of such studies, as well as the consistency of the empirical studies that rely on these ratings. (shrink)

This article describes the Metaphorical Source Domain Identification Procedure (MSDIP), which can be used to code source domains in metaphor identification. In the first part of the article, we describe the complexity of source-domain coding in corpus analysis. We argue that, in many cases, discourse is underspecified and multiple source-domain candidates may be relevant for a specific metaphorical expression. For instance, if a word like “fight” or “target” is used metaphorically, it could refer to either the source domain of war (...) or sports. To make these issues explicit for analysts, we developed MSDIP, which builds on and extends the Metaphor Identification Procedure Vrije Universiteit (MIPVU). In the second part of the article, we explain the coding steps of MSDIP and subsequently report on a reliability analysis, demonstrating the reproducibility of the procedure. We end with a number of detailed sample analyses, demonstrating the role of co-text and context in selecting the likeliest source-domain candidate through MSDIP. These analyses show that MSDIP is both reliable and flexible in dealing with the complexities of real-life discourse during source-domain coding. (shrink)