Este trabalho objetiva apresentar um novo campo que emergiu a partir da intersecção entre a Linguística de Corpus e a Pragmática: a Pragmática de Corpus. Para tanto, através de uma revisão da literatura como ponto de partida, traçamos um panorama que abarca a origem, os aspectos teórico-metodológicos, e os desafios da nova área. Ademais, introduzimos as abordagens forma-função e função-forma, dois modelos investigativos que integram a disciplina. Finalmente, por meio de um estudo de caso, a fim de ilustrar um dos possíveis percursos de análise, investigamos o marcador pragmático kind of por meio da filtragem, método que compõe a abordagem forma-função, no discurso oral de brasileiros universitários. Os subcorpora que subsidiam a pesquisa são o Spoken Corpus of Brazilian Portuguese and L2-English (SCoPE²) e o Brazilian Spoken English Learner Corpus (BraSEL). Os resultados apontam que quando usado pragmaticamente, mesmo em contextos linguísticos distintos, kind of ocorre em seus três domínios funcionais (atitudinal, interpessoal, textual) e como parte constituinte de marcadores de linguagem vaga.

We propose to embed time series in a latent space where pairwise Euclidean distances (EDs) between samples are equal to pairwise dissimilarities in the original space, for a given dissimilarity measure. To this end, we use auto-encoder (AE) and encoder-only neural networks to learn elastic dissimilarity measures, e.g., dynamic time warping (DTW), that are central to time series classification (Bagnall et al., 2017). The learned representations are used in the context of one-class classification (Mauceri et al., 2020) on the datasets of UCR/UEA archive (Dau et al., 2019). Using a 1-nearest neighbor (1NN) classifier, we show that learned representations allow classification performance that is close to that of raw data, but in a space of substantially lower dimensionality. This implies substantial and compelling savings in terms of computational and storage requirements for nearest neighbor time series classification.

Emerging evidence suggests that social identities are an important determinant of adaptation following traumatic life experiences. In this paper, we analyse accounts of people who experienced child sexual abuse. Using publicly available talk of people who waived their right to anonymity following successful conviction of perpetrators, we conducted a thematic analysis focusing on trauma-related changes in their social identities. Analysis of these accounts highlighted two themes. The first highlights the acquisition in these accounts of unwanted and damaging identity labels. The second presents child sexual abuse as a key destructive force in terms of important identity work during childhood. Discussion of this analysis centres on the pathological consequences of social identity change. Both the loss of valued identities and the acquisition of aberrant and isolating identities are experienced and constructed as devastating by those affected by child sexual abuse. This has important implications, not only for those impacted by child sexual abuse but for how abuse is discussed in society, and how it is approached by policy makers, educators and individuals working with survivors and their families.

It is shown that most of the existing versions of the Bhatnagar-Gross-Krook model-those whose coefficient are independent of the molecular velocity-do not satisfy the Onsager relations. This circumstance poses a problem when calibrating these models, making their transport properties match those of a specific fluid.

Abstract Amid COVID-19 and the so-called “digital pivot”, online virtual communication is at the heart of our professional and private lives. As we move into a post-COVID context, the affordances of the digital turn have shown that we can operate professionally online but there is a need for better understanding of communication in the online workplace. This paper contributes to our understanding of the dynamics of indicators of engagement in multi-party communication online, as evidenced by a corpus-based multi-modal study. It showcases the importance of building naturally-occurring spoken corpora that go beyond written transcription and include annotation of non-verbal behaviour. The work focuses on the incidence, frequency, position, and function of spoken and head nod backchannels, exploring coordination and co-occurrence of these features in online talk. Findings point to a changing profile of how engagement is displayed in online workplace meetings, which appears to be linked to the functionality of platforms.

Purpose: Broadly speaking, student voice can be defined as initiatives that involve consultation of, feedback from, and engagement with students regarding their own education. This study’s aim was to explore teachers’ experiences and perceptions of enacting student voice in primary physical education. Method: Participants included six primary school health and physical education specialist teachers within Victoria, Australia. Data were collected via six rich and detailed one-on-one semistructured interviews. Results: Three main themes include: (a) “same-same but different” highlighting participants’ varying conceptualizations and enactment of student voice, (b) “language matters” emphasizing the importance of language used when discussing and implementing student voice, and (c) “barriers and challenges to implementation” capturing participants’ experience and limiting factors to the enactment of student voice practices. Discussion/Conclusion: Grounded in education for transformation and patterns of partnership theories, the discussion focuses on the disassociation between teachers’ perceived understanding and enactment and the implications for students resulting from the misalignment.

BACKGROUND: Aphasia is an impairment of language as a result of brain damage which can affect individuals after a stroke. Recent research in aphasia has highlighted new technologies and techniques that fall under the umbrella of big data and artificial intelligence (AI). OBJECTIVES: This review aims to examine the extent, range and nature of available research on big data and AI relating to aphasia post stroke. METHODS: A mapping review is the most appropriate format for reviewing the evidence on a broad and emerging topic such as big data and AI in post-stroke aphasia. Following a systematic search of online databases and a two-stage screening process, data was extracted from the included studies. This analysis process included grouping the research into inductively created categories as the different areas within the research topic became apparent. RESULTS: Seventy-two studies were included in the review. The results showed an emergent body of research made up of meta-analyses and quasi-experimental studies falling into defined categories within big data and AI in post-stroke aphasia. The two largest categories were automation, including automated assessment and diagnosis as well as automatic speech recognition, and prediction and association, largely through symptom-lesion mapping and meta-analysis. CONCLUSIONS: The framework of categories within the research field of big data and AI in post-stroke aphasia suggest this broad topic has the potential to make an increasing contribution to aphasia research. Further research is needed to evaluate the specific areas within big data and AI in aphasia in terms of efficacy and accuracy within defined categories.

Confidence interval performance is typically assessed in terms of two criteria: coverage probability and interval width (or margin of error). In this article, we assess the performance of four common proportion interval estimators: the Wald, Clopper-Pearson (exact), Wilson and Agresti-Coull, in the context of rare-event probabilities. We define the interval precision in terms of a relative margin of error which ensures consistency with the magnitude of the proportion. Thus, confidence interval estimators are assessed in terms of achieving a desired coverage probability whilst simultaneously satisfying the specified relative margin of error. We illustrate the importance of considering both coverage probability and relative margin of error when estimating rare-event proportions, and show that within this framework, all four interval estimators perform somewhat similarly for a given sample size and confidence level. We identify relative margin of error values that result in satisfactory coverage while being conservative in terms of sample size requirements, and hence suggest a range of values that can be adopted in practice. The proposed relative margin of error scheme is evaluated analytically, by simulation, and by application to a number of recent studies from the literature.

The diatom's frustule, characterized by its rugged and porous exterior, exhibits a remarkable biomimetic morphology attributable to its highly ordered pores, extensive surface area, and unique architecture. Despite these advantages, the toxicity and nonbiodegradable nature of silica-based organisms pose a significant challenge when attempting to utilize these organisms as nanotopographically functionalized microparticles in the realm of biomedicine. In this study, we addressed this limitation by modulating the chemical composition of diatom microparticles by modulating the active silica metabolic uptake mechanism while maintaining their intricate three-dimensional architecture through calcium incorporation into living diatoms. Here, the diatom Thalassiosira weissflogii was chemically modified to replace its silica composition with a biodegradable calcium template, while simultaneously preserving the unique three-dimensional (3D) frustule structure with hierarchical patterns of pores and nanoscale architectural features, which was evident by the deposition of calcium as calcium carbonate. Calcium hydroxide is incorporated into the exoskeleton through the active mechanism of calcium uptake via a carbon-concentrating mechanism, without altering the microstructure. Our findings suggest that calcium-modified diatoms hold potential as a nature-inspired delivery system for immunotherapy through antibody-specific binding.