Computational social science with confidence

IF 3 2区计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

EPJ Data Science Pub Date : 2024-01-10 DOI:10.1140/epjds/s13688-023-00435-0

Carolina E. S. Mattsson

引用次数: 0

Abstract

There is an ongoing shift in computational social science towards validating our methodologies and improving the reliability of our findings. This is tremendously exciting in that we are moving beyond exploration, towards a fuller integration with theory in social science. We stand poised to advance also new, better theory. But, as we look towards this future we must also work to update our conventions around training, hiring, and funding to suit our maturing field.

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自信的计算社会科学

计算社会科学正在向验证我们的方法和提高我们研究结果的可靠性转变。这是非常令人兴奋的，因为我们正在超越探索，走向与社会科学理论更充分的结合。我们还准备推进新的、更好的理论。但是，在展望未来的同时，我们也必须努力更新我们在培训、招聘和资金方面的惯例，以适应我们不断成熟的领域。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -

CiteScore

6.10

自引率

5.60%

发文量

审稿时长

13 weeks

期刊介绍： EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.