In this talk I present the main results from Anta (2021), namely, that the theoretical division between Boltzmannian and Gibbsian statistical mechanics should be understood as a separation in the epistemic capabilities of this physical discipline. In particular, while from the Boltzmannian framework one can generate powerful explanations of thermal processes by appealing to their microdynamics, from the Gibbsian framework one can predict observable values in a computationally effective way. Finally, I argue that this statistical mechanical schism contradicts the Hempelian (...) (1958) thesis that the predictive power of a scientific theory is directly proportional to its explanatory potential, and vice versa. (shrink)

It is well‐established within linguistics that slang serves a group‐identifying function. In this paper, a new understanding of the notion of lexical metadata is developed to provide a philosophical treatment of said function. The proposed account explains the group‐identifying function of slang in terms of certain inferences about a speaker's group affiliations that people competent with a slang word will be disposed to make given the lexical metadata related to the word in question. The resulting view is theoretically simple and (...) may extend to a whole range of linguistic phenomena that speakers utilize to position themselves in social space. (shrink)

Silence can be a communicative act. Tanesini (2018) demonstrates how “eloquent” silences can virtuously indicate resistance and dissent; in this paper, I outline one way silence can also be used viciously to cause discursive harm, specifically by slurring victims. By distinguishing between eloquent and “signaling” silences (two kinds of what I call “performative” silences), I show how “slurring” silences — fully quiet discursive moves that signal one's commitment to a slurring perspective — function in a manner that illuminates the pragmatic (...) topography of conversational silence in general. (shrink)

Abstract‘The other’ is one of the mysteries of modern philosophy. Since the other is thought to be essentially different from the self, how we can understand each other is a difficult problem. In the first place, what does it mean to understand the other? I address this question from the perspective of normative inferentialism, by explicating what it means to understand the other's beliefs and actions. I propose that we should distinguish between attributional and fundamental understanding. While attributional understanding serves (...) to specify the other's ways of thinking, fundamental understanding is an endorsement of them. Contrasting these two types of understanding gives us a rational picture of how people understand each other. (shrink)

The current study represents an integrated model of explicit and implicit offensive language taxonomy. First, it focuses on a definitional revision and enrichment of the explicit offensive language taxonomy by reviewing the collection of available corpora and comparing tagging schemas applied there. The study relies mainly on the categories originally proposed by Zampieri et al. (2019) in terms of offensive language categorization schemata. After the explanation of semantic differences between particular concepts used in the tagging systems and the analysis of (...) theoretical frameworks, a finite set of classes is presented, which cover aspects of offensive language representation along with linguistically sound explanations (Lewandowska-Tomaszczyk et al. 2021). In the analytic procedure, offensive from non-offensive discourse is first distinguished, with the question of offence Target and the following categorization levels and sublevels. Based on the relevant data generated from Sketch Engine (https://www.sketchengine.eu/ententen-english-corpus/), we propose the concept of offensive language as a superordinate category in our system with a number of hierarchically arranged 17 subcategories. The categories are taxonomically structured into 4 levels and verified with the use of neural-based (lexical) embeddings. Together with a taxonomy of implicit offensive language and its subcategorization levels which has received little scholarly attention until now, the categorization is exemplified in samples of offensive discourses in selected English social media materials, i.e., publicly available 25 web-based hate speech datasets (consult Appendix 1 for a complete list). The offensive category levels (types of offence, targets, etc.) and aspects (offensive language property clusters) as well as the categories of explicitness and implicitness are discussed in the study and the computationally verified integrated explicit and implicit offensive language taxonomy proposed in the study. (shrink)