Glen Berman

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.

This article considers how the emerging Artificial Intelligence (AI) research field is constructed, primarily in university settings. AI research is a site of significant national funding, industry investment, and media interest. As such, for researchers working across the resource-constrained science system, their relationship to the field is significant; legitimisation as an AI researcher can bring material and symbolic rewards. Through interviews (n = 90) with academics affiliated with AI-branded research organisations in the US, UK, and Australia, the article develops an empirical account of the construction of AI research as a high-dimensional field – a field that moves between multiple disciplinary and sectoral boundaries across national and international hierarchies. The article draws on the sociology of expertise and studies of research infrastructures to develop the conceptual frame of dimensionality to explain the vertical and horizontal dynamics informing the AI field's development. The article's contributions are its description of the emerging AI field, which complements critical studies of how the figure of AI is mobilised in other settings, and its extension of field theory to fluid spaces that leverage the boundary zone between several overlapping field arrangements.

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.