Do Article Influence scores overestimate the citation impact of social science journals in subfields that are related to higher-impact natural science disciplines?

Unlike Impact Factors (IF), Article Influence (AI) scores assign greater weight to citations that appear in highly cited journals. The natural sciences tend to have higher citation rates than the social sciences. We might therefore expect that relative to IF, AI overestimates the citation impact of social science journals in subfields that are related to (and presumably cited in) higher-impact natural science disciplines. This study evaluates that assertion through a set of simple and multiple regressions covering seven social science disciplines: anthropology, communication, economics, education, library and information science, psychology, and sociology. Contrary to expectations, AI underestimates 5IF (five-year Impact Factor) for journals in science-related subfields such as scientific communication, science education, scientometrics, biopsychology, and medical sociology. Journals in these subfields have low AI scores relative to their 5IF values. Moreover, the effect of science-related status is considerable—typically 0.60 5IF units or 0.50 SD. This effect is independent of the more general finding that AI scores underestimate 5IF for higher-impact journals. It is also independent of the very modest curvilinearity in the relationship between AI and 5IF.     

对高校管理进行司法审查的现实诉求与路径选择

在现行的高校管理中,从理论角度看,存在高校管理主体法律地位模糊、事业单位公共服务功能与行政管理职能同构、彰显学术自由权力和发挥司法审查作用失衡等难题;从实践层面看,存在高校管理主体与相对人纷争"诉讼难"、高校管理司法审查"范围窄"、高校利用学术权力掩护对司法审查进行"主动规避"等障碍。这都亟须作为他律机制的司法审查制度的介入:确立高校管理司法审查的基本原则,重新审视高校主体资格的法律地位,勘定高校管理运行司法审查的"合理域"。     

行为经济学视角下终身学习困境的破解路径

终身学习强调持续性的学习过程,对于提高人口素质以适应经济社会高质量发展具有重要意义。然而,个体在终身学习中常表现出学习行动滞后于实际需求的现象,这一现实层面的终身学习困境制约了其成效的发挥。从行为经济学视角来看,终身学习需求与实际行动不一致的原因在于学习者存在短视认知偏差,这使其更看重学习的短期收益与成本的对比,而对长期效用考虑较少。因此,为破解终身学习困境,可通过降低相对学习成本来提升学习的主动性,提升社会成员的受教育水平和信息技术素养以纠正和调节短视认知偏差,并采取锁定机制对短视认知偏差进行约束。基于中国综合社会调查数据的实证分析结果表明上述路径切实有效。未来,应当注重多元主体参与以丰富终身学习资源供给和降低学习成本,构建高质量的教育体系以提升社会成员的学校教育水平和信息技术素养,强化"技能—工作"匹配以纠正终身学习中的短视认知偏差。     

Speciesist language and nonhuman animal bias in English Masked Language Models

Warning: This paper contains examples of offensive language, including insulting or objectifying expressions.Various existing studies have analyzed what social biases are inherited by NLP models. These biases may directly or indirectly harm people, therefore previous studies have focused only on human attributes. However, until recently no research on social biases in NLP regarding nonhumans existed. In this paper,¹ we analyze biases to nonhuman animals, i.e. speciesist bias, inherent in English Masked Language Models such as BERT. We analyzed speciesist bias against 46 animal names using template-based and corpus-extracted sentences containing speciesist (or non-speciesist) language. We found that pre-trained masked language models tend to associate harmful words with nonhuman animals and have a bias toward using speciesist language for some nonhuman animal names. Our code for reproducing the experiments will be made available on GitHub.²     

关于行政司法审查中法律解释方法的思考——以指导性行政案例为研究对象

法律解释方法在不同的司法领域有着不同的特性。从对指导性案例的分析来看,我国行政司法审查中法律解释方法具有一定的缺陷,诸如忽视行政法解释的价值取向、元规则缺失、运用水平低等。由此,在今后的行政司法审查中,我们应当依据行政法特性选择解释方法,努力形成解释方法元规则并积极遵循,提高运用解释方法时的说理论证能力,以弥补行政法解释理论之不足,摆脱行政司法审查解释的困境,推进中国行政法治的发展。