生存悖论和困境下的白大省形象简析

铁凝的中篇小说《永远有多远》的主人公白大省是一个天性善良、仁义的女性,她的核心性格就是善良。如果说白大省的善良是人性光辉的体现,铁凝在此也毫不犹豫地彰显自己对善良的推崇。铁凝把白大省放置在爱情经历、与同性交往和家人关系中,诠释彰显她本真的善良本性,以及在现在和未来的生存角逐中无所适从的悲剧,在白大省的生存悖论和困境中诠释了她丰富的形象。     

《荀子》论辩推理探究

荀子作为先秦时期伟大的思想家,其《荀子》一书涉及内容广泛,论辩严谨与荀子的学说主张相关。荀子认为,君子必辩,注重"虑"、"谋"使文章具有说服力。从逻辑推理的角度,从演绎推理、归纳推理、类比推理三个方面分析了《荀子》的推理方式,窥豹其论辩魅力。     

教育管理效率化批判

纵观教育管理史以及通过理论分析,我们发现教育管理效率化是一个悖论.悖论之一是以“经济人”理论为基础,悖论之二是教师工作是可以量化的,悖论之三是把学生看作教育产品,悖论之四是把学校看作科层组织.     

Learning the principles of simulation using the birthday problem

Using the famous Birthday problem, we present here a practical activity that allows students to perceive the basic reasoning behind simulation and explore its potential. Through a playful approach with probabilities, students are led along a path that illustrates difficulties with intuition and introduces them to theoretical results for sample proportions.     

上证A股市场风险与收益关系的实证研究

本文以上证(SHSE)A股市场为考察对象,利用Shefrin和Statman的行为证券组合模型,对影响上证A股横截面预期收益的关键因素进行了探索性研究,据此实证检验了上证A股市场风险与收益之间的关系。基于行为证券组合的实证模型,考虑了上证A股市场特有的收益影响因子。采用2000年2月~2004年6月上证A股418家上市公司为样本,实证结果表明,上证A股市场风险与收益之间存在显著的负相关关系。这与利用标准金融模型的实证结果不同,但却趋于和上证A股市场实际情境吻合。     

范例推理在文本自动分类中的应用研究

文本自动分类是文本信息处理中的一项基础性工作。将范例推理应用于文本分类中,并利用词语间的词共现信息从文本中抽取主题词和频繁词共现项目集,以及借助聚类算法对范例库进行索引,实现了基于范例推理的文本自动分类系统。实验表明,与基于TFIDF的文本表示方法和最近邻分类算法相比,基于词共现信息的文本表示方法和范例库的聚类索引能有效地改善分类的准确性和效率,从而拓宽了范例推理的应用领域。     

中国如何走出科技创新困境———基于科技创新与人力资本协同发展的新视角

本文立足于中国科技创新投入高速增长、全要素生产率增速放缓的典型事实，从科技创新与人力资本协同发展的视角，探讨如何破解科技创新困境的问题。首先分析了科技创新与人力资本相互作用的协同机理，基于中国制造业行业面板数据，实证检验了科技创新与人力资本的协同效应，并探讨了两者协同效应的行业差异。研究发现：单独自主研发创新、合作研发创新、非研发创新均没有提升制造业绿色全要素生产率，但三者与人力资本的协同互动具有显著促进效应，这说明实现科技创新与人力资本协同发展，是破解中国科技创新困境、“研发—生产率悖论”以及促进经济高质量发展的关键；人力资本与科技创新的协同效应存在行业差异，高技术行业中两者的协同效应不明显，这可能与高技术行业需要更高的人力资本与之协同有关。本文的发现为党的十九大提出的建设“协同发展的产业体系”提供了理论依据，并且对中国走出科技创新困境、实现经济高质量发展具有政策参考意义。     

Causal Inference and COVID: Contrasting Methods for Evaluating Pandemic Impacts Using State Assessments

In the spring of 2021, just 1 year after schools were forced to close for COVID-19, state assessments were administered at great expense to provide data about impacts of the pandemic on student learning and to help target resources where they were most needed. Using state assessment data from Colorado, this article describes the biggest threats to making valid inferences about student learning to study pandemic impacts using state assessment data: measurement artifacts affecting the comparability of scores, secular trends, and changes in the tested population. The article compares three statistical approaches (the Fair Trend, baseline student growth percentiles, and multiple regression with demographic covariates) that can support more valid inferences about student learning during the pandemic and in other scenarios in which the tested population changes over time. All three approaches lead to similar inferences about statewide student performance but can lead to very different inferences about student subgroups. Results show that controlling statistically for prepandemic demographic differences can reverse the conclusions about groups most affected by the pandemic and decisions about prioritizing resources.     

Media Covid misinformation due to confounding

We discuss a case study on how misinformation regarding Covid-19 health outcomes can arise due to confounding. Data from the UK on mortality rates suggest that people who have some level of vaccination and contract the Delta variant of Covid are twice as likely to die than those who are unvaccinated. Age, however, a confounding variable, when accounted for, produces a more complicated picture. The mortality rates for the vaccinated are statistically lower than the unvaccinated for the older but not younger age group. We present several approaches for teaching confounding to help students better understand this underemphasized concept's cause, effects, and origins.