Volume 20, Issue 3 - Special Issue: 20 years of CLLT; Guest Editors: Stefan Th. Gries and Stefanie Wulff

In this introduction to the special issue celebrating CLLT’s 20th anniversary, we look back and forward in time. To look back, we present the results of a (tongue-in-cheek) corpus-linguistic analysis of about 10 years worth of data of research published in CLLT , IJCL , and Corpora in order to distill the “essence” of CLLT for the reader. As an added bonus, we use the opportunity to discuss ways to improve established ways of performing keyness analyses. To look forward, we asked six (teams of) researchers who all have shaped corpus linguistics and thus the journal to give us their take on what the most significant developments in the field have been, and where they see the most impactful opportunities and challenges arise. This introduction briefly summarizes their contributions.

This paper aims to give an overview of corpus-based research that investigates processes of language change from the theoretical perspective of Construction Grammar. Starting in the early 2000s, a dynamic community of researchers has come together in order to contribute to this effort. Among the different lines of work that have characterized this enterprise, this paper discusses the respective roles of qualitative approaches, diachronic collostructional analysis, multivariate techniques, distributional semantic models, and analyses of network structure. The paper tries to contextualize these approaches and to offer pointers for future research.

During the past 20 years, corpus linguistic research on register variation has yielded important theoretical advances. The first part of this paper discusses these advances and the cumulative body of research that has produced them. In the second part of the paper, we focus on the goals of research on register variation. The traditional goal of the text-linguistic (TxtLx) approach to linguistic variation has been to describe registers and patterns of register variation: describing the linguistic and situational characteristics of registers. In this paper, we explore a related, but distinct, text-linguistic goal: to account for all linguistic variation among texts. Because the TxtLx framework assumes the importance of functional correspondence between linguistic characteristics and situational characteristics, it is reasonable to assume that in addition to register, we can use situational parameters coded continuously at the level of individual texts as additional predictors of text-linguistic variation. We describe the results of an empirical study to show that using both register categories and text-level situational parameters as predictors results in a more comprehensive and explanatory model of text-linguistic variation. In the conclusion we discuss the future of corpus-based register studies, focusing on unanswered questions related to theoretical claims about register.

Recent years have seen an increase in data and method reflection in corpus-based discourse analysis. In this article, we first take stock of some of the issues arising from such reflection (covering concepts such as triangulation, objectivity/subjectivity, replication, transparency, reflexivity, consistency). We then introduce a new ‘accountability’ framework for use in corpus-based discourse analysis (and perhaps beyond). We conceptualise such accountability as a multi-faceted phenomenon, covering various aspects of the research process. In the second part of this article, we then link this framework to a new cross-institutional initiative – the Australian Text Analytics Platform (ATAP) – which aims to address a small part of the framework, namely the transparency of analyses through Jupyter notebooks. We introduce the Quotation Tool as an example ATAP notebook of particular relevance to corpus-based discourse analysis. We reflect on how this notebook fosters accountability in relation to transparency of analysis and illustrate key applications using a set of different corpora.

Over the past decade, learner corpora have gained recognition as valuable data sources in Second Language Acquisition (SLA) research. This development can be attributed to significant progress in Learner Corpus Research (LCR). However, there is still substantial work to be done. This article highlights key issues essential for sustaining the relevance of learner corpora in SLA. More particularly, I focus on the need for more diverse types of learner corpora, stress the importance of detailed metadata, and advocate for multifactorial study designs. I then revisit ongoing debates regarding the role of the native speaker in LCR and propose a practical solution to address this thorny issue. Finally, I also readdress the need for improvement in the quantitative methods and statistics, arguing that the importance of robust quantitative analysis cannot be overstated. In conclusion, I envision an ambitious learner corpus compilation project that adheres to the FAIR principles, with the goal of further elevating study quality in LCR.

Corpus linguistics, with its methodological orientation towards the empirical analysis of language based on large text collections, has the potential to offer significant tools for addressing real-world problems across various social science domains, including climate change, criminology, healthcare and policy making. Despite this potential, the integration of corpus linguistics into social science disciplines (beyond linguistics) remains hampered by fundamental differences in epistemology, definitions and methodological approaches. This article explores the relationship between corpus linguistics and the social sciences. It is argued that epistemology, or the theory of knowledge, represents a primary barrier to integration, with much corpus linguistics research aligning with positivist and naturalist epistemologies. By contrast, many social science disciplines embrace more interpretive, conventionalist approaches that account for the dynamic nature of social phenomena. Considering the role of naturalism and conventionalism within both corpus linguistics and the social sciences, this article illustrates how these epistemological stances are likely to influence the acceptance and use of corpus methods in social science research. Despite the challenges, areas of convergence (e.g. shared use of data processing tools and the acknowledgement of the central role of language in social processes) provide opportunities for cross-disciplinary collaboration. As means to bridge the epistemological divide, this article advocates for a critical realist approach and concludes by calling on users of corpus linguistic methods to be reflexive and transparent about their epistemological stances when reporting their research.