Investigating hybridity in artificial intelligence research

Abstract

Research in the global field of artificial intelligence is increasingly hybrid in orientation. Researchers are beholden to the requirements of multiple intersecting spheres, such as scholarly, public, and commercial, each with their own language and logic. Relatedly, collaboration across disciplinary, sector and national borders is increasingly expected, or required. Using a dataset of 93,482 artificial intelligence publications, this article operationalises scholarly, public, and commercial spheres through citations, news mentions, and patent mentions, respectively. High performing publications (99th percentile) for each metric were separated into eight categories of influence. These comprised four blended categories of influence (news, patents and citations; news and patents; news and citations; patents and citations) and three single categories of influence (citations; news; patents), in addition to the ‘Other’ category of non-high performing publications. The article develops and applies two components of a new hybridity lens: evaluative hybridity and generative hybridity. Using multinomial logistic regression, selected aspects of knowledge production – research context, focus, artefacts, and collaborative configurations – were examined. The results elucidate key characteristics of knowledge production in the artificial intelligence field and demonstrate the utility of the proposed lens.

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.