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With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

Recent research has captured the impact of the COVID-19 pandemic in widening gender inequalities, by highlighting that academic women have been disproportionately affected. During the COVID-19 pandemic, women assumed most of the care labour at home, whilst working at normal patterns, leaving them unable to perform as normal. This is very concerning because of the short and long-term detrimental consequences this will have on women’s well-being and their academic careers. This article aims to stimulate a change in the current understandings of academic work by pointing towards alternative – and more inclusive – ways of working in academia.

The two authors engage with autoethnography and draw on their own personal experience of becoming breastfeeding academic mothers throughout the COVID-19 pandemic.

To understand the positioning of contemporary academic mothers, this study draws on insights from both cultural studies and organisation studies on the emergence of discursive formations about gender, that is “postfeminist sensibility”. Guided by autoethnographic accounts of academic motherhood, this study reveals that today academia creates an individualised, neutral (disembodied), output-focused and control-oriented understanding of academic work.

This paper adds to the conversation of academic motherhood and the impact of the pandemic on working mothers. The study theoretically contributes with the lens of “motherhood” in grasping what academic work can become. It shows the power of motherhood in opening up an alternative way of conceptualising academic work, centred on embodied care and appreciative of the non-linearity and messiness of life.

One of the longest running protests in recent American history was a Sing-Along in the Wisconsin State Capitol Building. This daily informal gathering to sing protest songs began in 2011, then prompted a sudden wave of arrests beginning in 2013. Instead of dwindling, the protest grew in response as participants celebrated resistance, treating arrest as a local in-group status symbol. This chapter uses extended participant observation, a methodological approach rarely found in the social movement literature on repression, to study the attempted repression of this Solidarity Sing-Along. To a remarkable extent, arrests and court prosecutions were ineptly executed. This ineptitude had consequences for the protest's development. This repression was also generally mild. Examining mild repression, less often studied than severe forms, helps elaborate the range of repression's potential consequences. By showing mild repression in ethnographic detail, this chapter reveals an underappreciated messiness on the part of both repressors and repressed. The movement evolved in a messy way in response to messy repression, an evolution that is not well captured with dichotomous categories of increase versus decrease or failure versus success.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This chapter considers the role and significance of ‘wellness’ as an idealised image, mode of being and subjecthood connected to ‘a perfect life’ in neoliberal Western contexts, which is made particularly visible through social media platforms such as Instagram. I discuss how ‘wellness’ is attached to particular bodily styles of presentation and appearance, such as the ‘Instagram influencer’, drawing on data from a qualitative study that used interview and digital photo-voice methods to explore how young people make sense of and encounter ‘perfect social media bodies’. I draw on feminist new materialist understandings of the body as socially and materially co-produced to theorise the body as assembled through the socio-material conditions of everyday life. This theorisation contributes to emerging efforts to interrogate the sociological and material dynamics of ‘wellness’ assemblages as important contemporary modes through which bodies (particularly connected to gendered aspects of feminine bodies) are felt and lived. Importantly, the gendered bodily appearances coded as representing an ‘ideal life’ and ‘perfect body’, which align with comportments of ‘wellness’, are central for understanding how aesthetic capital and bodily value are attributed in a Western neoliberal context. This analysis aims to contribute to feminist analyses of the affective and socio-material dynamics through which bodies and images ‘become’ through each other. The chapter concludes with an examination of the paradoxical and jarring dimensions signalled in the promises of wellness as a pursuit towards attaining an ‘ideal life’ against the backdrop of late capitalism and impending climate collapse.