Editorial: Innovation measurement for scientific communication (IMSC) in the era of big data
| Date | 31 October 2024 |
| Pages | 849-853 |
| DOI | https://doi.org/10.1108/EL-12-2024-353 |
| Published date | 31 October 2024 |
| Author | Zhongyi Wang,Haihua Chen,Chengzhi Zhang,Wei Lu,Jian Wu |
Editorial: Innovation measurement
for scientific communication
(IMSC) in the era of big data
Introduction
Scientific communication playsa central role in addressing many of the pressing challenges
facing society today (Achiam et al.,2022). In this context, the measurement of innovation
within scientific communication is crucialfor both understanding and advancing knowledge
in today’sfast-paced, data-driven world (Wang et al., 2023).However, measuring innovation
accurately is a complex endeavour. The rapid growth of scientific output and the increasing
complexity of interdisciplinary research make it challenging to identify and assess truly
ground-breakingwork.
Artificial intelligence (AI) has already proven its effectiveness across a range of societal
applications (Barakat et al., 2021). AI, including large language models (LLMs), excels in
identifying patterns from vast data sets and generating accurate predictions based on these
insights (Ni et al., 2024;Rubungo et al., 2023;Xuet al.,2024). In the scientific domain, AI
has demonstrated remarkable capabilities in synthesis, comprehension, reasoning and
interpretation, showcasing its potential to revolutionise research processes (Krenn et al.,
2022;Xu et al., 2021;Zheng et al., 2023). By harnessing these capabilities, AI offers
promising new approachesfor evaluating innovation more effectively and accurately.LLMs,
as a subset of AI, are particularly well-suited for handling complex linguistic data, allowing
for nuanced understanding and generation of scientific discourse. Their ability to process
interdisciplinary knowledge and integrate information across domains makes LLMs
essential tools in assessinginnovation across various scientificfields.
Developing robust methods for measuring innovation is essential, not only for
recognising pioneering contributions in scientific communication but also for deepening our
understanding of research impact and fostering a culture of continuous advancement within
the academic community. As we navigate the era of big data, the integration of AI into
innovation measurement tools is critical –not only for identifying significant scientific
contributions but also for enhancing our understanding of how innovation emerges and
spreads across the scientificlandscape.
Moreover, communication lies at the heart of science, permeating every stage of the
research process and enabling scientists to share their findings with peers through journal
articles, preprints and conference proceedings (Russell, 2001). The rise of information
technology, particularly personal computers and the World Wide Web, has transformed
scientific communication, shiftingit from a traditional print-based systemreliant on
scientific journals to one increasingly dependent on electronic communication and digital
storage (Hurd, 2000). As a result, the measurement of innovation in scientific
communication frequently involves analysing digital journal articles, preprints and other
electronic formats to assess their impactandcontributions to the field.
For example, Wang et al. (2024b) developed a content-based evaluation method for scientific
papers that focuses on integrity, clarity, novelty and significance, thereby enhancing innovation
This paper forms part of a special section “Innovation measurement for scientific communication (IMSC) in
the era of big data”, guest edited by Zhongyi Wang,Haihua Chen, Chengzhi Zhang, Wei Lu and Jian Wu.
The Electronic
Library
849
TheElectronic Library
Vol.42 No. 6, 2024
pp. 849-853
© Emerald Publishing Limited
0264-0473
DOI 10.1108/EL-12-2024-353
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