Research diversification and its relationship with publication counts and impact: A case study based on Australian professors is a research paper published in Journal of Information Science (2019). On theSindex it has a DataRank of 0.618. It has been cited 14 times, with 12 citing works in its 1-hop citation network.
This research aims to investigate whether multi/inter-disciplinary research activities are related to research impact and publication counts of scholars. Since researchers with very high levels of multi/inter-disciplinarity might be able to target complex problems, we would expect them to receive more credits than their colleagues with a stronger disciplinary orientation. We analysed Web of Science (WoS) indexed publications of all associate and full professors from a random sample of Australian universities in physics, chemistry and biology (1980–2014). Australian Fields of Research (FoR) codes assigned to journals were used to calculate the diversification of authors’ publications. The number of citations in the first 3 years, number of 10% most frequently cited papers, and citation impact percentile were used for impact assessment. A few indicators were used to measure the diversity including ‘extent of diversification (ED)’ (number of distinct FoR codes divided by the number of publications) and ‘diversification ratio (DR)’ (ratio of the publications falling outside the dominant code to the total number of publications). A total of 47.76% of biologists’ publications, 35.23% of physicists’ publications and 20.36% of chemists’ publications were published in journals assigned to fields other than the Australian associate and full professors’ fields. Publications from biologists had the largest values of diversification. Women (compared with men) and associate professors (compared with full professors) in chemistry, biology and overall were more probably to publish diversely. ED was negatively correlated with output and citation impact. DR also had a negative but weak correlation with the number of publications and 10% most frequently cited paper.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.406
From this paper's citation signal
Citation Network Contribution
0.212
From 9 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 66% comes from its base citations and 34% from the citation network (9 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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