Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications

Matteo Lascialfari (), Marie-Benoît Magrini () and Guillaume Cabanac ()
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Matteo Lascialfari: AGIR, Université de Toulouse, INRAE
Marie-Benoît Magrini: AGIR, Université de Toulouse, INRAE
Guillaume Cabanac: IRIT, Université de Toulouse, CNRS

Scientometrics, 2022, vol. 127, issue 11, No 7, 6165-6189

Abstract: Abstract Lock-in and path-dependency are well-known concepts in economics dealing with unbalanced development of alternative options. Lock-in was studied in various sectors, considering production or consumption sides. Lock-in in academic research went little addressed. Yet, science develops through knowledge accumulation and cross-fertilisation of research topics, that could lead to similar phenomena when some topics do not sufficiently benefit from accumulation mechanisms, reducing innovation opportunities from the concerned field consequently. We introduce an original method to explore these phenomena by comparing topic trajectories in research fields according to strong or weak accumulative processes over time. We combine the concepts of ‘niche’ and ‘mainstream’ from transition studies with scientometric tools to revisit Callon’s strategic diagram with a diachronic perspective of topic clusters over time. Considering the trajectories of semantic clusters, derived from titles and authors’ keywords extracted from scholarly publications in the Web of Science, we applied our method to two competing research fields in food sciences and technology related to pulses and soya over the last 60 years worldwide. These highly interesting species for the sustainability of agrifood systems experienced unbalanced development and thus is under-debated. Our analysis confirms that food research for soya was more dynamic than for pulses: soya topic clusters revealed a stronger accumulative research path by cumulating mainstream positions while pulses research did not meet the same success. This attempt to unpack research lock-in for evaluating the competition dynamics of scientific fields over time calls for future works, by strengthening the method and testing it on other research fields. Graphical abstract

Keywords: Bibliometric data; Path-dependency; Natural language processing; Science mapping; Sustainability concern (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11192-022-04514-3

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