女性主义:本土化及其维度

2 0世纪80年代以来,女性主义在中国本土化进程中出现了诸多误区与盲点,使其存在的合理性与合法性受到质疑,主要表现为:将本土女性的特殊生存经验误读为西方女性主义的前身,将本土理论提炼简化为西方女性主义的单向传播,将女性生存的丰富内蕴缩减为单一的女性/男性对峙结构。要使女性主义实现真正的本土化,至少须在三方面努力:在参照系的定位上,从制造镜像转向观照自身;在中西女性主义的差异理解上,从价值判断回到学理判断;在资源的确定上,从碎片拼贴转向内外整合。     

柏拉图语言哲学探析

柏拉图被公认为西方语言哲学的思想先驱。他在多篇对话中深入探讨了语言哲学的基本问题:如名称的指称问题、陈述的真假问题、语言和实在的关系问题等等。这些探讨对于后世语言哲学家的工作具有重要的启示和规范作用。     

弘扬优秀传统文化精神 培养社会主义核心价值观

优秀的传统文化,在中华民族的发展史中,其精神始终存在,在任何时候都没有泯灭。习近平总书记就很注重优秀传统文化传承和对青年社会主义核心价值观的培养。本文阐释了优秀传统文化的基本内涵,就如何从传统文化精神出发,培养社会主义核心价值观进行了探讨。     

二语学术写作引用研究述评

文章从二语习得和二语使用两个视角对二语学术写作引用研究的现状及其成果进行了梳理并发现：学术引用能力是在读写认知过程中逐渐发展起来的,且受到学术写作知识、批判性思维和文本借用策略的影响;引用的类型、修辞功能及其学术语体在专家与新手的学术写作中存在差异。     

A stochastic model of organizational mortality: Review and reanalysis

An effort is made to integrate the research literatures of business policy and organizational sociology as they concern organizational mortality. The previous empirical studies of organizational mortality are reviewed and considered in light of current theoretical arguments. Three stochastic models are developed to test hypotheses concerning organizational mortality: the constant rate model, the Gompertz model, and Makeham's Law. The parameters of these models are estimated for 52 sets of data on organizational mortality. The findings show that Makeham's Law is the best-fitting model, although its estimation requires data with low levels of censoring. Substantively, the findings show strong support for Stinchombe's liability-of-newness hypothesis [A. L. Stinchcombe (1965), “Organizations and social structure,” in Handbook of Organizations (J. G. March, Ed.), pp. 153–193, Rand McNally, Chicago].     

对“中文屋”思想实验四个主要版本的考察

"中文屋"思想实验提出30年来,学界对该思想实验在批判强人工智能的有效性问题上一直争论不休。特别是强人工智能的支持者往往通过转换视角,添加问题或构建新理论模型等方式试图躲避"中文屋"的责难。为应对各种回应,塞尔在保持"中文屋"论证的基本逻辑结构和要点不变的前提下,通过修改思想实验的描述形式进行一一答复,从而保证了该思想实验对强人工智能批判上的效力。借助对"中文屋"几个主要版本及其相关答复的考察,表明"中文屋"在批判强人工智能上的有效性,同时指出这种有效性并不依赖于思想实验外在描述形式的变化,而依赖于蕴含其中的论证结构和论证形式。     

Part I––Comparing Noncancer Chronic Human Health Reference Values: An Analysis of Science Policy Choices

The goal of this study was to systematically evaluate the choices made in deriving a chronic oral noncancer human health reference value (HHRV) for a given chemical by different organizations, specifically those from the U.S. Environmental Protection Agency, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This analysis presents a methodological approach for comparing both the HHRVs and the specific choices made in the process of deriving an HHRV across these organizations. Overall, across the 96 unique chemicals and 171 two‐way organizational comparisons, the HHRV agreed approximately 26% of the time. A qualitative method for identifying the primary factors influencing these HHRV differences was also developed, using arrays of HHRVs across organizations for the same chemical. The primary factors identified were disagreement on the critical or principal study and differential application of the total uncertainty factor across organizations. Of the cases where the total UF was the primary factor influencing HHRV disagreement, the database UF had the greatest influence.     

Part II: Quantitative Evaluation of Choices Used in Setting Noncancer Chronic Human Health Reference Values Across Organizations

Environmental and public health organizations, including the World Health Organization (WHO) and the U.S. Environmental Protection Agency (USEPA), develop human health reference values (HHRV) that set “safe” levels of exposure to noncarcinogens. Here, we systematically analyze chronic HHRVs from four organizations: USEPA, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This study is an extension of our earlier work and both closely examines the choices made in setting HHRVs and presents a quantitative method for identifying the primary factors influencing HHRV agreement or disagreement.⁽¹⁾ We evaluated 171 organizational comparisons, developing a quantitative method for identifying the factors to which HHRV agreement (that is, when both organizations considering the same data set the identical HHRV values) is most sensitive. To conduct this analysis, a Bayesian belief network was built using expert judgment, including the specific science policy choices analysis made in the context of setting an HHRV. Based on a sensitivity of findings analysis, HHRV agreement is most sensitive to the point of departure value, followed by the total uncertainty factor (UF), critical study, critical effect, animal model, and point of departure approach. This analysis also considered the specific impacts of individual UFs, with the database UF and the subchronic‐to‐chronic UF being identified as primary factors impacting the total UF differences observed across organizations. The sensitivity of findings analysis results were strengthened and confirmed by frequency analyses evaluating which choices most often disagreed when the HHRV and the total UF disagreed.     

也谈参考文献的作用及其引用问题

参考文献是论文的重要组成部分。认为参考文献有六方面的作用,提出了参考文献的引用原则,并就当前参考文献的引用问题进行了分析。     

Noninformative priors for the generalized half-normal distribution

In this paper, we develop noninformative priors for the generalized half-normal distribution when scale and shape parameters are of interest, respectively. Especially, we develop the first and second order matching priors for both parameters. For the shape parameter, we reveal that the second order matching prior is a highest posterior density (HPD) matching prior and a cumulative distribution function (CDF) matching prior. In addition, it matches the alternative coverage probabilities up to the second order. For the scale parameter, we reveal that the second order matching prior is neither a HPD matching prior nor a CDF matching prior. Also, it does not match the alternative coverage probabilities up to the second order. For both parameters, we present that the one-at-a-time reference prior is a second order matching prior. However, Jeffreys’ prior is neither a first nor a second order matching prior. Methods are illustrated with both a simulation study and a real data set.