Towards a plurilingual habitus: engendering interlinguality in urban spaces

This article focuses on the potential of the multilingual city to create spaces in which monolingual hegemonies may be challenged, inclusive, intercultural values may be nurtured, and plurilingualism may be valorised. Following a contextualisation of linguistic diversity in theories of globalisation and superdiversity, discourses of deficit and power are addressed, arguing that the problematisation of multilingualism and pathologisation of plurilingualism reflect a monolingual habitus. Bringing about a shift towards a plurilingual habitus requires a Deep Approach, as it involves a critical revaluing of deep-seated dispositions. It suggests that the city offers spaces, which can engender interlinguality, a construct that includes interculturality, criticality and a commitment to creative and flexible use of other languages in shared, pluralistic spaces. It then proposes critical, participatory and ethnographic research in three multidimensional spaces: the urban school and a potential interlingual curriculum; networks, lobbying for inclusive policy and organising celebratory events in public spaces; and grass roots-level local spaces, some created by linguistic communities to exercise agency and maintain their languages and cultures, and some emerging as linguistically hybrid spaces for convivial encounter

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work