因为机器可以区分:机器学习如何为人类差异的生物学解释提供服务和转化
1Department of Sociology, University of Chicago, Chicago, IL, USA.
概括
研究方法,如机器学习,可以制度化科学思想. 在神经科学中,机器学习通过提供合适的定量方法来帮助性别差异运动,使该领域复兴.
科学领域:
- 科学社会学科学社会学
- 神经科学是一个神经科学.
- 量化方法 量化方法
背景情况:
- 科学运动往往专注于外部资源,如资金,忽视内部的方法影响.
- 作为机构的技术的Pinch理论为理解方法如何塑造科学运动提供了一个框架.
研究的目的:
- 论证研究方法可以作为科学运动的机构资源.
- 通过检查神经科学中机器学习的采用及其对性别差异运动的影响来证明这一点.
主要方法:
- 在神经科学中采用机器学习的分析.
- 检查"性别差异运动"及其依赖于特定的定量方法.
- 将Pinch的技术理论作为机构的应用.
主要成果:
- 机器学习在神经科学中的采用提供了一个"真正的分类"定量方法.
- 这种方法与性别差异运动对不同男性和女性大脑/身体的看法一致.
- 该运动经历了一次复苏,出版和合作增加.
结论:
- 研究方法可以制度化想法,并作为科学运动的关键资源.
- 机器学习的影响可能会扩展到其他基于群体的科学概念,如种族和性.
- 由于技术的社会后果,社会学家对技术内部运作的关注是合理的.
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