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The purpose of the chapter is to explain how boys learn to mask their expression of fears and emotions. The aim is to understand how boys internalize socially prescribed masculine traits, including masking of fear, certain emotional expressions and discomfort. The sample consisted of 20 parents, 30 school teachers and 50 boy students and 50 girl students between the ages of 11 and 14 from government-funded co-education schools in Delhi, India. School observation, focus group discussion, and interviews were used for data collection. The study found that gendered social norms are enforced on boys in the form of ‘boy codes’. These boy codes are so deep rooted in daily practices that they are considered as an essential ‘ideal male’ trait. Although the ‘ideal male image’ is presented as a uniform category among boys, the masking of fears and emotional expressions is not the same for all boys. Thus, many boys internalized the ideal male images in the form of hegemonic displays of masculinity, where they are focused on conforming to rigid masculine traits. However, through challenge, negotiation and renegotiation, many boys would like to conduct themselves according to their personal masculinity. There is a shift among some boys from the internalization of the traditional male image to giving meaning to personal experiences that deviate from the ideal male figure without the fear of being judged by society.

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.