The influence of 'forgotten' scientific papers has been demonstrated in a new study led by a researcher from Goldsmiths, University of London.
A team from Goldsmiths, the University of Chicago, Google, the University of Maryland, and Columbia University, developed a model that tracks ‘discursive influence’, or recurring words and phrases in historical texts that measure how scholars actually talk about a field, instead of just their attributions. To determine a particular scientific paper’s influence, the researchers can statistically remove it from history and see how scientific discourse would have unfolded without it.
Aaron Gerow, Lecturer in Computing at Goldsmiths, who led the study said: “Citations are one kind of impact, and discursive influence is a different kind. Neither one is the complete story, but they work together to give a better picture of what’s influencing science.”
The researchers report in the journal PNAS how they trained the model on massive text collections from computational linguistics, physics, and across science and scholarship (JSTOR) and then traced distinct patterns of influence. They found that scientists who persistently published in a single field were more likely to be ‘canonised’ in a way that compelled others to cite them disproportionate to their papers’ discursive contributions. On the other hand, discoveries that crossed disciplinary boundaries were more likely to have outsized discursive impact but fewer citations, likely because the ‘owner’ of the idea and her allies remain socially and institutionally distant from the citing author.
The model also sheds light on so-called ‘sleeping beauties’: papers that went relatively unacknowledged for years or even decades before experiencing a late burst of citations. For example, a 1947 paper on graphene remained obscure and forgotten until the 1990s with a resurgence of research interest in the material and an eventual Nobel Prize.
Study co-author James Evans, director of Knowledge Lab and professor of sociology at the University of Chicago, said: “Papers have a news cycle, when lots of people chat about them and cite them, and then they’re no longer new news. Our model shows that some papers have much more influence than citations will typically demonstrate, such as these ‘sleeping beauties,’ which didn’t have much influence early but come to be appreciated and important later."
The study used a computational method known as ‘topic modeling’ that was invented by co-author David Blei of Columbia University. The authors said the same model can also be used to trace influence in other areas, such as literature and music. Text from poems or song lyrics, and even extra-textual characteristics such as stanza structure or chord progressions, could feed into the model to find under-credited influencers and map the spread of new concepts and innovations.
A report of the research, ‘Measuring discursive influence across scholarship’ by Aaron Gerow, Yuening Hu, Jordan Boyd-Graber, David M. Blei and James A. Evans, is published in Proceedings of the National Academy of Sciences.