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The purpose of this study is to identify key school leadership practices that center social justice and are evidenced across multiple school and district contexts.

A qualitative, multidistrict research design is used within this study. Sampled across seven US school districts, 24 school leaders were interviewed about their justice-centered school leadership practices within and outside of their school buildings.

Participants engaged in five key domains of justice-centered practice, which included 13 practice areas and 28 sub-practices. These domains include (1) creating an inclusive and caring environment, (2) promoting equitable opportunity to learn, (3) strengthening staff capacity for justice, (4) positioning families as partners in education and (5) building and extending community capacity and resources.

This study incorporates empirical data across diverse contexts and investigates actions relevant to diverse students and multiple justice-centered leadership approaches. By doing so, this study unearths a spectrum of justice-centered school leadership practices, presenting these in one of the few empirically grounded frameworks available in the literature. This framework provides an accessible, comprehensive and actionable starting place for practitioners hoping to lead in socially just ways and for preparation programs who will support these leaders.

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.