The article presents a new language learner corpus for Swedish, SweLL, and the methodology from collection and pesudonymisation to protect personal information of learners to annotation adapted to second language learning. The main aim is to deliver a well-annotated corpus of essays written by second language learners of Swedish and make it available for research through a browsable environment. To that end, a new annotation tool and a new project management tool have been implemented, – both with the main purpose to ensure reliability and quality of the final corpus. In the article we discuss reasoning behind metadata selection, principles of gold corpus compilation and argue for separation of normalization from correction annotation.

This article explores processes of place-making through the study of the linguistic landscape of a small-size town in Northern Sweden. The analysis of signs is used as a tool for examining the role and visibility of actors in the landscape. For this purpose, we examine who the authors are, what forms of multilingualism can be observed, and who has agency in the place-making of the public space. Our documentation consists of photos and fieldnotes from observations, encounters, and conversations with people during ethnographic fieldwork in 2019. Using a mixed-methods approach, all signs were first analysed quantitatively according to the categories of authors and function. Regression analysis was used to explore correlations between the categories. Secondly, multilingual signs were analysed qualitatively regarding their function and purpose in relation to their contexts. Our results illustrate a city centre with a strong presence of the Swedish language. Multilingual signs target specific groups and are intended for information, advertisement, rules and regulations; moreover, our findings indicate that the opportunities for private actors to influence the linguistic landscape are limited. The form of multilingualism in this context—visible multilingualism present mainly through English—is different from the one we can see in the socio-demographic data.

No abstract