Search results

The latest novel coronavirus disease 2019 (COVID-19) pandemic continues to have a significant social and financial impact globally. It is very essential to study, categorize and systematize published research on mitigation strategies adopted during previous pandemic scenario that could provide an insight into improving the current crisis. The goal of this paper is to systematize and identify gaps in previous research and suggest potential recommendations as a conceptual framework from a strategic point of view.

A systematic review of Scopus and Web of Science (WoS) core collection databases was performed based on strict keyword search selections followed by a bibliometric meta-analysis of the final dataset.

This study indicated that the traditional mitigation techniques adopted during past pandemics are in place but are not capable of managing the transmission capability and virulence of COVID-19. There is a greater need for rethinking and re-engineering short and long-term approaches to prevent, control and contain the current pandemic situation.

Integrating various mitigation approaches shall assist in flattening the pandemic curve and help in the long run.

Articles, conference proceedings, books, book chapters and other references from two extensive databases (Scopus and WoS) were purposively considered for this study. The search was confined to the selected keywords outlined in the methodology section of this paper.

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.