一个社会科学信任分类学与新兴向量和对称性
Anthony E D Mobbs1, Simon Boag1
1School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
Frontiers in psychology
|September 16, 2024
概括
这项研究提出了基于自我信任和他人信任的社会科学统一本体学,为词汇分类创建了一个25个单元的矩阵. 这种框架使精确的社会科学测量成为可能,并统一了各种领域.
科学领域:
- 社会科学 社会科学 社会科学
- 心理学 心理学 心理学
- 语言学的语言学.
背景情况:
- 信任是社会科学的基本概念,但缺乏统一的理论或衡量标准.
- 现有研究强调,缺乏一个凝聚力的框架来理解信任及其行为表现.
- 本研究探讨了需要一个共同的本体基础来整合社会科学学科的需求.
研究的目的:
- 为社会科学发展一个统一的本体学,以信任的概念为基础.
- 为社会科学研究建立一个一致的测量基础.
- 通过一个新的词典和分类学来探索信任,统治和合作之间的关系.
主要方法:
- 扩大了现有的统治和归属词汇词汇库,达到3万多个术语.
- 利用渐变下降机器学习进行分类学优化,使用t-test作为目标函数.
- 识别了具有共同根源的单词类型 (例如动词和形容词) 之间的词向量和新型对称性.
主要成果:
- 开发了一个基于自我信任和他人信任轴的25个单元本体矩阵,对所有词典单词进行分类.
- 产生了超过226,000个词向量,揭示了诸如恶意-意义-温柔-仁慈的关系.
- 发现了动词 (原因) 和形容词 (效果) 之间的对称性,这些动词有共同的根源,比如"拒绝"和"被拒绝".
结论:
- 拟议的基于信任的本体学为社会科学中的心理测试提供了一个计量学上合规的基础.
- 识别的词向量和对称性为社会互动动态提供了洞察力,使得更高的准确性和精确性.
- 这个框架有可能统一社会科学领域,并将它们与自然科学方法相协调.
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