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There has been an increase in the number of people experiencing gender dysphoria and neurodiversity over the last decade. Medical studies employing quantitative methodologies consistently report a high co-occurrence of gender diversity and neurodiversity. Simultaneously various sociocultural views have been proposed to understand these conditions together. Still, there is limited evidence on how this co-occurrence is experienced by individuals with gender dysphoria. This article aims to investigate how gender dysphoria and neurodivergent conditions, specifically autism, are articulated and perceived by people aged 15-35 experiencing gender dysphoria in Sweden. Sixteen semi-structured interviews, conducted between August 2023 and March 2024, were analyzed using thematic analysis following Braun and Clarke's approach. According to the themes identified in our data, the participants recognized themselves as divergent, with some suspecting that they might be neurodivergent. They were also familiar with the commonly reported co-occurrence of gender diversity and neurodiversity. Lastly, our participants discussed the intersectional relation between gender diversity and neurodiversity predominantly as a social rather than a clinical phenomenon, with neurodiversity in some instances seen as facilitating gender diversity. Our findings may reflect a newly emerging perspective on how the co-existence of gender diversity and neurodiversity is interpreted by those experiencing gender dysphoria.

This article centers on the difficulties of conducting social and life sciences research in a low-income country: Mozambique. It draws on decolonial, professionalization and self-motivation theory to examine, through qualitative, participative, discussion-based, bottom-up research with local informants carried out between December 2024 and February 2025, what factors inhibit the conduct of research in this context. The article suggests that it is less the self-motivation of the informants than contextual factors which make research difficult. These factors include the significant discrepancies between the abstract workload model of a 40-h week and the actual experiences of working as an academic where many professional activities take much more time than imagined in the abstract workload model. Many professional activities are, further, unrecognized in this workload model and, importantly, also unremunerated-important in a context where basic salaries are low and need to be bumped up by additional labor, either within the employing institution or, very commonly, outside. A further factor in making research difficult is sociocultural, a result of the hierarchist, patriarchal culture that prevails and which subordinates women and junior staff such that they are at the beck and call of more senior and male staff who can call on them at any time. This makes the planning of time to conduct research very difficult. All this is compounded by the poor state of the research infrastructure with very limited facilities and intermittent access to the internet, a must in the contemporary knowledge economy. The article ends with a series of recommendations at micro, meso and macro level to remedy this situation.

This article investigates the pervasive issue of bias within AI-driven cultural heritage collections, emphasizing how digital technologies both inherit and amplify existing societal and historical prejudices embedded in analogue records. It outlines the multifaceted nature of bias—ranging from data selection and annotation to algorithmic design and user interaction—demonstrating how each stage of the AI pipeline can introduce or perpetuate distortions in representation. Through a critical review of current scholarship and practical case studies, particularly in image classification, the article evaluates technical strategies such as data augmentation, adversarial debiasing, and monitoring plans for bias mitigation. The findings reveal that while methods like noise injection and colour jittering can balance datasets and improve model fairness, effective bias mitigation ultimately depends on interdisciplinary collaboration between heritage professionals, subject experts, and data scientists. The article concludes that addressing bias requires an ongoing, holistic approach, integrating both technical and humanistic perspectives from data collection to model deployment. This ensures more inclusive, accurate, and ethically sound representations of cultural heritage, supporting the sector’s goals of diversity and accessibility for future audiences.

This article examines the challenges and opportunities that arise with artificial intelligence (AI) and machine learning (ML) methods and tools when implemented within cultural heritage institutions (CHIs), focusing on three selected Swedish case studies. The article centres on the perspectives of the CHI professionals who deliver that implementation. Its purpose is to elucidate how CHI professionals respond to the opportunities and challenges AI/ML provides. The three Swedish CHIs discussed here represent different organizational frameworks and have different types of collections, while sharing, to some extent, a similar position in terms of the use of AI/ML tools and methodologies. The overarching question of this article is what is the state of knowledge about AI/ML among Swedish CHI professionals, and what are the related issues? To answer this question, we draw on (1) semi-structured interviews with CHI professionals, (2) individual CHI website information, and (3) CHI-internal digitization protocols and digitalization strategies, to provide a nuanced analysis of both professional and organisational processes concerning the implementation of AI/ML methods and tools. Our study indicates that AI/ML implementation is in many ways at the very early stages of implementation in Swedish CHIs. The CHI professionals are affected in their AI/ML engagement by four key issues that emerged in the interviews: their institutional and professional knowledge regarding AI/ML; the specificities of their collections and associated digitization and digitalization issues; issues around personnel; and issues around AI/ML resources. The article suggests that a national CHI strategy for AI/ML might be helpful as would be knowledge-, expertise-, and potentially personnel- and resource-sharing to move beyond the constraints that the CHIs face in implementing AI/ML.

Our article AI, cultural heritage, and bias examines the challenges and potential solutions for using machine learning to interpret and classify human memory and cultural heritage artifacts. We argue that bias is inherent in cultural heritage collections (CHCs) and their digital versions and that AI pipelines may amplify this bias. We hypothesise that effective AI methods require vast, well-annotated datasets with structured metadata, which CHCs often lack due to diverse digitisation practices and limited interconnectivity. This paper discusses the definition of bias in CHCs and other datasets, exploring how it stems from training data and insufficient humanities expertise in generative platforms. We conclude that scholarship, guidelines, and policies on AI and CHCs should address bias as both inherent and augmented by AI technologies. We recommend implementing bias mitigation techniques throughout the process, from collection to curation, to support meaningful curation, embrace diversity, and cater to future heritage audiences.

The notion of experimental theatre is tied up with experiments in form and expression (e.g. Caryl Churchill’s works for theatre). Much of this work is by white playwrights and responds to western traditions of playwriting, performance, style such as the ‘well made play’ and realist drama. In its narrowest definition the term experimental theatre therefore references particular potentially Eurocentric but radical traditions of theatre making that are entangled with other more conservative forms of theatre, neither of which, within the British context, have created much room for theatre by practitioners of Black or Asian origin. This is partly a function of the use of classification as such (Foucault) which has the task of creating difference, or specificity, and at the same time a certain unity of assumption as to what theatre is. In theatre classification operates to delimit the scope of a given entity, foreclosing its horizon by assigning it to a particular order. Thus we talk of Black theatre but not, for instance, of white theatre. Classifications pigeonhole, privileging a particular dimension of a play while downplaying or ignoring others, with attendant implied asymmetric meanings. Against this, and in the contemporary spirit of decolonization, this chapter examines the plays of Gurpreet Kaur Bhatti, not as an experiment in form, staging or expression which would simply mean the reproduction of an existing Eurocentric order of theatre making, but as the portrayal of an experiment in living, interrogating contemporary forms of post-multicultural co-habitation. The chapter argues that Kaur Bhatti’s work seeks to decolonize conventional theatre classifications through its exploration of a post-multicultural Britain that struggles to come to terms with its heritage and which produces experiments in living whose sustainability is in question.

This article draws on an ethnographic study of an e-science platform in Sweden to analyse how horizontal gender segregation across sciences plays out in e-science, a borderland in which sciences converge around state-of-the art computational technologies for scientific research. While the convergence of sciences in e-science has the potential to open a non-traditional trajectory to attract women to ICTs, we find that this potential remains untapped. Instead horizontal gender segregation is perpetuated through a) restricted mobility of women from scientific fields with higher gender parity to IT, b) gender friction negatively affecting women in cross-disciplinary e-science, c) a gendered developer/user divide permeating e-science collaborations under 'the logic of domains,' and d) perceived self-reliance in computational tool development across sciences acting as 'gendered boundary work' to strengthen the gendered hard/soft divide in sciences.

This chapter centres on the vicissitudes of funding encountered in a context where a newly emerging discipline with specific gendered contours, in this instance digital humanities (DH), seeks to establish itself within universities at a time of shifting funding regimes. Drawing on interviews conducted with 30 DH practitioners in Sweden, Norway and Finland in 2017–2018, but focusing on the Swedish context, the chapter explores how gendered staffing structures (‘all the researchers are women; all the techies are men’), newness of discipline and changing funding regimes conjoin to create precarious work conditions for those embarking on work in DH. It explores the particular forms of precarity this conjunction takes in a country that publicly professes gender equality and has strong histories of worker protection.

Med denna Festskrift firar vi att Centrum för genusvetenskap i över fyrtio år har fungerat som en viktig mötesplats för kunskap och vetenskapande – en sådan plats som många uppfattar som absolut nödvändig för sin överlevnad i akademin. Genom att sätta genus i centrum på Centrum skapar vi kunskap över gränser mellan vetenskapsområden, fakulteter och forskningsfält.

Centrum består i grund och botten av de människor som verkar där. Den här Festskriften är också en hyllning till alla dessa människor – deras arbete, engagemang, intelligens, uthållighet och inspirationsförmåga. Centrum existerar också tack vare de viktiga nationella och internationella nätverk inom forskning och utbildning som Centrum är del av. Med denna bok skickar vi alltså en hälsning och ett tack till alla våra kollegor inom fältet. 

Kapitlen i denna volym är en blandning som sträcker sig från essäer och personliga historieskrivningar om Centrum, till reflektioner om arbetslivet vid Centrum eller som genusforskare överlag, till forskningsartiklar om en rad mycket olika områden, vilka i sig också vittnar om den livliga och variationsrika forskningsmiljö som vi fortsätter att bygga tillsammans.