Historic machines from 'prams' to 'Parliament': new avenues for collaborative linguistic research (original) (raw)

2022, CERN European Organization for Nuclear Research - Zenodo

Research in computational linguistics has made successful attempts at modelling word meaning at scale, but much remains to be done to put these computational models to the test of historical scholarship (see e.g. Beelen et al. 2021). More importantly, a lot of computational research looks at texts in a historical vacuum, 'synchronically', as linguists would say. Living with Machines is an interdisciplinary research project that rethinks the impact of technology on the lives of ordinary people during the Industrial Revolution (Ahnert et al. 2021). During this project, we decided to address a fundamental question: what did people mean by 'machine' and how has this meaning changed over time? This paper outlines how a simple research question like 'what was a machine?' can provide an opportunity to engage the public with our work while also generating data for analysis and new avenues of research in a radically collaborative way. Turning to a diachronic perspective, we wanted to capture how changes in the usage of this word in nineteenth century texts can help us understand the role of machines in nineteenth century imaginations. An earlier crowdsourcing task on the project defined machines as 'devices or equipment not powered by people or animals'. As a result of that task, we discovered that this definition did not reflect how 'machine' was used in contemporary newspaper articles. Accordingly, we designed the 'What's that machine?' citizen science tasks to find out what a 'machine' was in the 19th century as part of our linguistic and historical research. As engaging the public with our research is a key goal of the project, crowdsourcing, rather than internal annotation, was a natural fit. It also allowed us to tackle classification challenges at scale. We set up two related 'What's that machine?' tasks on the Zooniverse platform: Describe it! and Classify it! (Ridge, 2020). The former asked the public to transcribe excerpts from newspaper articles

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