Daniel C. S. Wilson

MapReader is an interdisciplinary software library for processing digitized maps (Hosseini et al., 2022a) and other types of images with two tasks: patch classification and text spotting. Patch classification works by 'patching' images into small, custom-sized cells which are then classified according to the user's needs. Text spotting detects and recognizes text. MapReader offers a flexible pipeline which can be used both for manual annotation of small datasets as well as for computer-vision-based inference of large collections. As an example, in Hosseini et al. (2022a), we annotated 62,020 patches, trained a suite of computer vision models and performed model inference on approximately 30.5 million patches.

This article introduces the ‘Environmental Scan’ as answer to the question of hidden biases in digital heritage collections. Its substantive focus is digitized nineteenth-century British provincial newspapers, and in particular the JISC corpus, a popular, publicly-funded resource for scholars. While multiple papers have meticulously investigated the genesis of such newspaper collections, in the process highlighting the often unacknowledged politics of collection, preservation and dissemination via microfilm and now digitization, our aim is to explore questions of representativeness and bias in new ways by enriching computational analysis of digital corpora with the historical insights that can be derived from a contemporaneous reference source: namely, the Victorian newspaper press directories.

Although the Ordnance Survey has itself been the subject of historical research, scholars have not systematically used its maps as primary sources of information. This is partly for disciplinary reasons and partly for the technical reason that high-quality maps have not until recently been available digitally, geo-referenced, and in color. A final, and crucial, addition has been the creation of item-level metadata which allows map collections to become corpora which can for the first time be interrogated en masse as source material. By applying new Computer Vision methods leveraging machine learning, we outline a research pipeline for working with thousands (rather than a handful) of maps at once, which enables new forms of historical inquiry based on spatial analysis. Our ‘patchwork method’ draws on the longstanding desire to adopt an overall or ‘complete’ view of a territory, and in so doing highlights certain parallels between the situation faced by today’s users of digitized maps, and a similar inflexion point faced by their predecessors in the nineteenth century, as the project to map the nation approached a form of completion.

Mariona Coll Ardanuy, Federico Nanni, Kaspar Beelen, Kasra Hosseini, Ruth Ahnert, Jon Lawrence, Katherine McDonough, Giorgia Tolfo, Daniel CS Wilson, Barbara McGillivray. Proceedings of the 28th International Conference on Computational Linguistics. 2020.

This article examines life assurance and the politics of ‘big data’ in mid-19th-century Britain. The datasets generated by life assurance companies were vast archives of information about human longevity. Actuaries distilled these archives into mortality tables – immensely valuable tools for predicting mortality and so pricing risk. The status of the mortality table was ambiguous, being both a public and a private object: often computed from company records they could also be extrapolated from public projects such as the census, or clerical records. Life assurance more generally straddled the line between private enterprise and collective endeavour, though its advocates stressed the public interest in its success. Reforming actuaries such as Thomas Rowe Edmonds wanted the data on which mortality tables were based to be made publicly available, but faced resistance. Such resistance undermined insurers’ claims to be scientific in spirit and hindered Edmonds’s personal quest for a law of mortality. Edmonds pushed instead for an open actuarial science alongside fellow travellers at the Statistical Society of London, which was populated by statisticians such as William Farr (whose subsequent work, it is argued, was influenced by Edmonds) as well as by radical mathematicians such as Charles Babbage. The article explores Babbage’s little-known foray into the world of insurance, both as a budding actuary but also as a fierce critic of the industry. These debates over the construction, ownership, and accessibility of insurance datasets show that concern about the politics of big data did not begin in the 21st century.