Marking imprecision, conveying surprise:Likebetween hedging and mirativity

Abstract

Mirative expressions, which mark surprising information (DeLancey 1997), are often expressed through linguistic forms that are also used to encode other, seemingly unrelated, meanings -e.g. evidential markers that mark lack of direct evidence (Turkish: Slobin among others). In this paper, we show that the English particle like features a parallel polysemy between a mirative use and its better-known hedging use, which expresses weakened commitment to the strict denotation of a linguistic expression. After presenting several diagnostics that point to a genuine empirical difference between the hedging and mirative functions of like, we propose that both uses widen the size of a contextually restricted set, admitting elements that were previously excluded. More specifically, hedging like expands the set of 'similar enough' interpretations that we can apply to a linguistic expression in a context, including interpretations that we would normally consider to be too different from the context at hand. Mirative like, on the other hand, expands the set of worlds that we are willing to consider as candidates for the actual world in the conversation, resulting in the inclusion of worlds that interlocutors have previously ruled out due to perceived outlandishness. We therefore suggest that the two uses are best treated as sharing a common semantic kernel, deriving hedging and mirativity as effects of the particular type of object to which like applies.

Context, precision, and social perception: A sociopragmatic study

“…Following each item, participants were asked to respond to ten evaluation questions about the speaker. Four questions pertain to the Status dimension: two of these scales-intelligent and articulate-had been shown to positively correlate with precision in Beltrama (2018); the other two-confident and trustworthy-were included building on the claim from the social psychology literature that they also correlate with high precision. Four scales-friendly, cool, laid-back, likable-pertain to the Solidarity dimension.…”

“…Finally, we include two attributespedantic, uptight-that, while also pertaining to Solidarity, express qualities that indicate a low rather than high value along this dimension. We thus group these attributes as part of the anti-Solidarity dimension; following the results from Beltrama (2018), we predict them to correlate with high precision. All scales included seven points.…”

“…By contrast, products described in round numbers are perceived as more stable and performing for a longer time, suggesting that a lower level of precision can also have positive associations (Pena-Marin & Bhargave 2016). Turning to sociolinguistic investigations, Beltrama (2018) finds that speakers describing events by means of sharp numbers are rated more highly than speakers using round numbers along two distinct clusters of attributes. One cluster includes traits such as 'articulate', 'intelligent', and 'educated'-a set of qualities often grouped under the category of Status in the sociolinguistic literature (see Milroy & Preston 1999 inter alia).…”

Rise of the machines? The evolving role of AI technologies in high-stakes assessment

“…As Bernstein et al (2010) claim, scoring models are data-driven and verifiable, often in ways that human scoring is not. AI systems are quicker, less prone to error and capable of assessing a number of key traits simultaneously; importantly, they do not carry any inherent halo or negative effects (Dennis, 2007;Beltrama and Hanink, 2019), and they treat all responses fairly -as long as the AI systems are trained on sound, representative samples. This is not to say, however, that AI can be used in all assessment areas.…”

Imprecision, personae, and pragmatic reasoning

“…While this process has been widely investigated in the semantic and pragmatic literature (see Dubois 1987;Lasersohn 1999;Van Der Henst, Carles & Sperber. 2002;Krifka 2007;Van Der Henst et al 2002;Kennedy 2007;Syrett et al 2009;Morzycki 2011;Leffel, Xiang & Kennedy 2016;Solt et al 2017;Aparicio 2017;Klecha 2018;Beltrama & Hanink 2018;Thomas & Deo 2020;Beltrama 2021), the question remains open as to what sources of contextual information comprehenders recruit in the process, and how this information is incorporated into the process leading to settling on their final interpretation.…”