Keywords
strategic scientists; empirical analysis; theme mining; LDA; theme model
Abstract
In the contemporary epoch, where technological prowess is increasingly intertwined with national competitiveness, strategic scientists emerge as pivotal figures in the realm of science and technology. These ‘scientific and technological talents with leadership’, a term that denotes their extraordinary intellectual capital and innovative capabilities, constitute a cornerstone in China’s quest for high-caliber technological autonomy and progressive enhancement. To further strengthen this foundation, there is a pressing need to systematically investigate the thematic foci and evolutionary trends characterizing research on strategic scientists, thereby informing and enriching the design and implementation of scientific and technological policies. This study embarks on a rigorous analytical journey by harnessing the combined power of Latent Dirichlet Allocation (LDA) topic modeling and Seasonal Autoregressive Integrated Moving Average ( SARIMA) prediction analysis. LDA, celebrated for its efficacy in unearthing latent thematic structures from vast corpora, is synergistically paired with SARIMA, a statistical forecasting tool adept at identifying and predicting cyclical patterns in time-series data. This multidimensional approach enables a comprehensive exploration and empirical analysis within the dynamic landscape of strategic scientist research. The application of these methodologies uncovers six cardinal themes that encapsulate the essence of inquiries related to strategic scientists. These themes span across realms such as the formation of a national strategic talent cadre, emphasizing not merely the execution of strategic national assignments by these scientists but also their pivotal role in architecting a robust strategic talent infrastructure. This underscores the recognition of strategic scientists as linchpins in the strategic planning and execution of national objectives, catalyzing advancements in technology and fostering an ecosystem conducive to sustained innovation. A pivotal and helpful finding from the SARIMA analysis reveals a discernible cyclical pattern in the research trends surrounding strategic scientists, indicating fluctuations in scholarly focus and resource allocation over time. This insight accentuates the necessity for anticipatory strategies that can maintain a steady momentum in research and development efforts, averting potential downturns during periods of waning interest. Prospective avenues of research, as illuminated by our analysis, point towards the innovative application of predictive models to identify, nurture, and optimally deploy strategic scientists. This forward-thinking approach seeks to leverage the quantitative forecasting capabilities of such models to anticipate talent requirements, thereby enabling a proactive and targeted approach to talent cultivation. Furthermore, the integration of new media technologies in disseminating scientific knowledge and fostering public education is posited as a transformative trend, capable of amplifying the societal impact of scientific breakthroughs and nurturing a culture of scientific literacy and innovation. In the quest for refining training systems tailored for strategic scientists, the paper concludes by advocating for a contemporaneous approach that aligns with the exigencies of the new era. This entails not only adapting educational curricula to reflect the latest advancements and challenges in the global scientific landscape but also fostering an environment that encourages interdisciplinary collaboration, promotes international engagement, and nurtures a spirit of entrepreneurship among strategic scientists. By doing so, China can ensure a steady pipeline of versatile and forward-thinking experts capable of steering the nation’s scientific and technological agenda towards unprecedented heights.
DOI
10.16315/j.stm.2024.03.008
Recommended Citation
HUANG, Tao and ZOU, Zhecan
(2024)
"Hotspot perspectives and future directions in the research field of strategic scientists based on LDA-SARIMA model,"
Journal of Science and Technology Management: Vol. 26:
Iss.
3, Article 3.
DOI: 10.16315/j.stm.2024.03.008
Available at:
https://jstm.researchcommons.org/journal/vol26/iss3/3
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