加强弱环:因果循环图的透明度,当前状态和建议
Mohammad S Jalali1,2, Elizabeth Beaulieu1
1Harvard Medical School, Boston, MA.
概括
在系统科学中评估因果循环图 (CLD) 显示了定性方法的透明度较低. 加强CLD报告对于推进系统科学研究和社区合作至关重要.
科学领域:
- 系统科学 系统科学
- 系统动态 系统动态
- 定性研究方法 定性研究方法
背景情况:
- 透明度在系统科学中至关重要,但因果循环图 (CLD) 等定性模型结构的审查比定量模型要少.
- 因果循环图 (CLD) 是系统动态中的一个关键可视化工具,用于表示复杂的因果关系.
研究的目的:
- 评估已发表的系统科学研究中因果循环图 (CLD) 的透明度.
- 为了提高透明度,确定需要改进的CLD报告的具体领域.
主要方法:
- 进行了72篇文章的系统审查,比较了"系统动力学评论" (SDR) 中的出版物与其他期刊的高度引用的文章.
- 透明度的评估是基于包括简单语言方法声明,方法的整体可辨识性和因果关系来源的识别.
主要成果:
- 只有44%的文章完全包含了简单的语言方法声明,38%的文章有可辨认的方法,25%的文章确定了因果关系来源.
- 与其他期刊相比,*SDR*文章在CLD开发方法和来源沟通方面显示出更高的透明度,尽管仍有很大的改进空间.
结论:
- 系统科学出版物中因果循环图 (CLD) 的透明度不足,需要改进报告标准.
- 实施透明的CLD报告建议将使研究人员受益,促进未来的模型开发,并加强系统科学界.
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