October 2021

This month reports summarises four articles that in/directly relate to the impact of universities. The first paper represents an overview of the current state of play to theories around research impact (Williams & Lewis, 2021) The second, is a book from the Philosopher of Science Nicholas Maxwell, asking the question is science neurotic? (Maxwell, 2004). The third investigated if co-authorship networks can be used in predicting research impact (Grodzinski et al. 2021). The last study investigated the suitability of altmetrics to predict research impact in the social sciences and humanities (Siluo et al. 2021).

[1]) Building on Williams’s earlier work around theorising research impact, they conclude that as a whole the idea, implementation, measurement and administration of research impact is woefully undertheorized. Research impact is often presented as a point to point causality (x causes y), when the causality is often complex, stemming from sustained engagement, multiple actors and stakeholders, influenced by changes beyond the control of the involved and subject to a multitude of interpretations. They argue, by taking a wider, in essence research approach to research impact, such simplifications and their unintended consequences can be mitigated.    

[2]) Maxwell diagnoses science to be a neurotic enterprise, where the supposed aims (truth) do not constitute with the actual conduct of researchers (using arguments for the benefit to society), and hence creates contradictions in the very logic of science that inhibit its social mission. According to him, only an honest and self-enlightened self-examination of this particular contradiction, represents a way to rescue science from itself and maintain its authority and potential for innovation. Otherwise, the inherent contradiction within the methodology (quest for truth) and application of its result (improving humanity) will fester, leading specialist researchers’ to undermine their own authority by their own actions.

[3]) Via a bibliometric analysis of 1674 journal articles, they investigated if co-authorship is indicative of what they call “key opinion leaders”. They never define the term, but state that they are “well-positioned to aid knowledge translation.” They are confident, that their neural network achieved a good predictive capabilities, as it was validated by an unseen author set retrospectively. Their findings, of the analysed medical papers (on Degenerative Cervical Myelopathy) showed that there was a strong geographical clustering to the collaborations (with North America, but not Western Europe). Secondly, according to their discussion, their model produces better results than traditional bibliometric analysis.

[4]) They utilised data from the website Altmetrics.com to investigate how useful it is to predict research impact within the social sciences and humanities. Their data set consisted of 1,253,023 data points over five years (2013-2017) for three types of publications (articles, reviews and proceedings papers) utilising 10 different altmetrics (tweets, Facebook posts, YouTube, etc.). They found that English publications are favoured. But concluded that the complex causality pathways of social sciences and humanities, meant that hitherto Altmetric represents an insufficient tool to make any substantial predictions.

[1] Williams, K., & Lewis, J. M. (2021). Understanding, measuring, and encouraging public policy research impact. Australian Journal of Public Administration.

[2] Maxwell, N. (2004). Is science neurotic?. Imperial College Press.

[3] Grodzinski, N., Grodzinski, B., & Davies, B. M. (2021). Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research. Plos one, 16(9), e0256997.

[4] Yang, S., Zheng, M., Yu, Y., & Wolfram, D. (2021). Are Altmetric. com scores effective for research impact evaluation in the social sciences and humanities?. Journal of Informetrics, 15(1), 101120.