Understanding the Future Green Workforce through a Corpus of Curricula Vitae from Recent Graduates

In view of the much-heralded ecological transition, to stay competitive and participate in the collective effort to face global warming and climate change, organisations need to select employees interested in and able to develop environmentally sustainable and innovative ideas. The existing literature however does not present consistent nor concordant results on the effective interest, involvement and expertise of Generation Z members – namely, the newest entrants into the workforce – in green issues. This study presents a corpus-assisted methodology to explore the profile of the upcoming workforce expected to present itself to companies. With CVs as one of the first interfaces between candidate and company in the recruitment process, a purpose-built corpus consisting of Curricula Vitae from recent graduates of the University of Modena and Reggio Emilia was collected. Data is investigated through a Corpus-Assisted Discourse Studies (CADS) framework, proposing a novel interaction between structured metadata and textual information. The original contribution of this approach lies in the extraction of information from the narrative structure of CVs which, guiding the evaluation and exploration of metadata, ensures that the knowledge value of the data can be explored in a discursive manner and not reduced to lists of competences and qualifications

Entrenchment inhibition:Constructional change and repetitive behaviour can be in competition with large-scale “recompositional” creativity

This paper addresses creativity as inhibition of repetitive behaviour. We argue that entrenchment and constructional change can be in competition with large-scale creative attempts of recomposition of constructions’ internal constituency. After undergoing chunking, the recurrent usage of a construction may be significantly counterbalanced with new attempts of entrenchment inhibition (viz. inhibition of entrenchment). These are cases where speakers opt for more compositional and less predictable ways to express a similar meaning of a conventionalised form. We focus on the constructionalisation of noun–participle compounds (e.g. snow-covered) in the Historical Corpus of American English. During the second part of the twentieth century, speakers increasingly inhibit the usage of conventionalised noun phrase–past participle forms in favour of more compositional strategies involving the same internal constituents. This entails that constructional change not only affects the meaning of the chunk that undergoes constructionalisation but also the way speakers creatively rediscover its internal constituency. These results additionally aim to inform research in cognitive architectures and artificial intelligence, where creativity is often merely considered as a problem-solving mechanism rather than a potential process of inhibition of automatised behaviour

MoReThesisCorpus

The article discusses the on-going process for the creation of the MoReThesisCorpus, outlining its major characteristics and offering an account of the considerations and issues involved so far. The corpus, composed of the theses submitted to the University of Modena and Reggio Emilia between 2011 and 2020, is being developed as part of the project CAP (‘Comunicazione Accademica e Professionale;’ Academic and Professional Communication), and is meant to foster research into academic language in a cross-disciplinary discourse perspective, as well as to facilitate the production of educational materials aimed at university students. It aims at supporting the acquisition of discipline-related vocabularies and styles to improve the learning of academic writing through corpus tools and resources, following a data-driven learning approach. Technical details surrounding the acquisition and subsequent processing of the data are discussed, along with considerations on a number of issues pertaining both to computer science and linguistics, directly impinging on the capability of the corpus to correctly support an investigation of academic discourse across different languages and disciplines

Pre-emptive interaction in language change and ontogeny:the case of [there is no NP]

This study is centred on the pre-emptive dimension of interactional exchanges. Dialogues are not merely characterised by information transmission, they are also constantly informed by pre-emptive attempts to address potential reactions to what is being said. We argue that pre-emptive interaction intersects with intersubjectivity (i.a. Traugott, Elizabeth C. 2003. From subjectification to intersubjectification. In R. Hickey (ed.), Motives for language change, 124–139. Cambridge: Cambridge University Press; Schwenter, Scott A. & Richard Waltereit. 2010. Presupposition accommodation and language change. In K. Davidse & L. Vandelanotte (eds.), Subjectification, intersubjectification and grammaticalization, 75–102. Berlin: De Gruyter Mouton; Tantucci, Vittorio. 2017a. From immediate to extended intersubjectification: A gradient approach to intersubjective awareness and semasiological change. Language and Cognition 9(1). 88–120; Tantucci, Vittorio. 2020. From co-actionality to extended intersubjectivity: Drawing on language change and ontogenetic development. Applied Linguistics 41(2). 185–214) and constitutes an important trigger of semantic-pragmatic reanalysis and constructional change. We provide a corpus-based study centred on the change of the [there is no NP] construction in Early Modern English dialogic interaction. During 16th century, the chunk is originally used in assertions, however it then progressively acquires a new function of pre-emptive refusal. Something similar is at stake throughout the child’s ontogeny. We provide corpus-based data from the CHILDES database of first language acquisition to show that children’s ability to use [there is no NP] to address potential reactions to what is being said occurs only around the fourth year of age, that is when a Theory of Mind (ToM) starts to become fully developed (i.a. Apperly, Ian. 2010. Mindreaders: The cognitive basis of theory of mind. New York: Psychology Press; Wellman, Henry M. 2014. Making minds: How theory of mind develops. Oxford: Oxford University Press). Pre-emptive interaction correlates diachronically and ontogentically with ToM and underpins a projected turn taking of a specific or generic interlocutor as a result of what is being currently said

Dynamic resonance and social reciprocity in language change:The case of Good morrow

Entrenchment (i.e. Langacker, 1987) does not necessarily lead to predictable behaviour. This study aims at complementing the usage-based model of language change by oper- ationalising the role of dialogic creativity as a mechanism that can be in competition with conventionalization and grammaticalization. We provide a distinctive collexeme analysis (i.e. Hilpert, 2006) focussing on the constructionalization of the dialogic pair [A: good morrow B e B: (good) morrow (A)] from the 15th up to the 18th century. After reaching the highest degree of entrenchment and automatisation, the dialogic pair will show an increasing tendency to be creatively re-modelled with ad-hoc meanings during online exchanges by means of dynamic resonance (Du Bois, 2014) and non-reciprocal behaviour. We define this creative process of large-scale alteration as entrenchment inhibition. From our data it will emerge that entrenchment inhibition is triggered by spontaneous attempts of producing a creative ‘surplus’ over the expected social reciprocity (Gouldner, 1960) of conventionalized exchanges. This tendency will be shown to be driven by marked attempts of polite and impolite behaviour

An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a significant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai