论“自主学习”模式下的教师角色

培养学习者自主学习的能力是外语教学的重要目标。本文在自主学习模式下,探讨当代大学教师应扮演的角色:策划、指导者;监控、评估者;促进、帮助者;反思、研究者;资源利用者和心理医生。     

地理距离、方言文化与劳动力空间流动

本文基于百度迁徙大数据研究中国劳动力的空间流动，系统考察地理和文化对劳动力流动的影响。通过构造流动机会比率，基于引力模型和普通最小二乘法的研究表明地理距离和方言距离阻碍劳动力流动。在空间距离上，劳动力偏好邻近城市，地理距离每增加1%，劳动力的流动机会比率降低约0.6%。在空间位置上，劳动力倾向于在方言文化相近地域范围流动，方言距离每增加1%，劳动力的流动机会比率下降2%左右。通过构造两地年均降水量差距和小麦种植适宜度差距作为方言距离的工具变量，以两阶段最小二乘法估计缓解内生性问题，估计显示结论稳健。考虑普通话因素后方言距离的抑制影响依然稳健，但目的地的高普通话普及率显著发挥促进劳动力流动的引力作用。最后，本文得出持续推广普通话、加强交通建设和深化中等教育的政策建议。     

递阶双因素模型估计信用组合一致性风险

当信用组合内资产个数较少时，基于Gordy提出的粒度调节方法计算的组合风险量度VaR将严重被低估，从而高估一致性风险量度预期短缺ES。本文利用Taylor展开式改进粒度调节方法，提高估计ES的准确性；并且假设行业因素是宏观经济因素的线性函数，由此保证了组合不变性的条件，从而扩展单因素模型为递阶双因素模型，提高相关性估计准确性，解决了单因素模型高估经济资本的问题。模拟结果显示递阶双因素模型的优越性，特别是组合内资产规模较小时，改进的效果更明显。     

口译笔记的认知理据

口译笔记是影响口译质量的一个重要因素。文章从认知科学的角度出发分析了口译笔记的特点和规律,指出口译笔记的具体操作和激活扩散模型、图式理论、连通主义等许多认知领域的研究成果有密切的联系,其中尤其是吉尔的精力分配理论,对如何做口译笔记有很强的实践指导作用。     

新时期榜样示范的出路

在社会转型的新时期下,随着价值观念日益趋向多元化,榜样示范面临着巨大的挑战和困境。如何洞悉榜样示范所面临的困境,并超越现实找到出路,是我们必须认真对待和深刻思考的。     

FTA 背景下新西兰对中国农产品出口增长分析

根据UN Comtrade数据库中贸易数据,采用恒定市场份额模型,对1997—2012年期间新西兰向中国农产品出口增长的影响因素进行了实证研究。结果表明,中国进口需求的增长是新西兰对中国农产品出口大幅增长的主要原因。在中国－新西兰自由贸易区建立后,增长效应对出口增长的带动作用明显减弱,商品效应和整体竞争效应贡献率有所回升,表明中国－新西兰自贸区建立后新西兰向中国出口的农产品结构有所优化,且在中国市场的整体竞争力提高;不同种类的农产品影响新西兰向中国出口农产品贸易额增长的因素有一定差异。     

均值—绝对偏差模型鲁棒优化策略的有效性——基于中国股票市场的实证分析

在不确定的金融市场中,由于各种金融产品风险存在差异,因此,如何在兼顾收益与风险的情况下对产品进行组合选择,也就成为投资组合的重要问题。通过将均值—绝对偏差模型的鲁棒优化模型与我国证券市场实际情况相结合的方法,提出简化模型,并且以MAT?LAB为工具,提出该线性模型求最优解的新方法。同时,将均值—绝对偏差模型的鲁棒优化模型的最优解与其他投资组合模型进行比较,证明该模型优于所选的其他模型。     

Social Relations Model Analyses of Perceived Self‐Control and Trust in Families

How do people know which family member is trustworthy? In this study, the authors tested the hypothesis that people use their perception of a family member's self‐control as an indicator of his or her trustworthiness. Eighty‐four Dutch families consisting of 2 parents and 2 children completed questionnaires assessing each family member's trust in and perceived self‐control of the other 3 family members. This full‐family design enabled the authors to examine their hypothesis in horizontal relationships, between family members of equal status (i.e., parent–parent and sibling–sibling relationships), and vertical relationships, in which partners have unequal status (i.e., parent–child and child–parent relationships). Consistent with the hypothesis, Social Relations Model analyses showed that being perceived as having higher self‐control is related to greater trustworthiness among adults and children in the large majority of horizontal and vertical relationships (10 out of 12). These findings highlight that perceived self‐control is an important factor by which to gauge trustworthiness in families.     

Model Selection Information Criteria in Latent Class Models with Missing Data and Contingency Question

Latent class analysis (LCA) has been found to have important applications in social and behavioral sciences for modeling categorical response variables, and nonresponse is typical when collecting data. In this study, the nonresponse mainly included “contingency questions” and real “missing data.” The primary objective of this research was to evaluate the effects of some potential factors on model selection indices in LCA with nonresponse data.

We simulated missing data with contingency questions and evaluated the accuracy rates of eight information criteria for selecting the correct models. The results showed that the main factors are latent class proportions, conditional probabilities, sample size, the number of items, the missing data rate, and the contingency data rate. Interactions of the conditional probabilities with class proportions, sample size, and the number of items are also significant. From our simulation results, the impact of missing data and contingency questions can be amended by increasing the sample size or the number of items.     

General Sparse Boosting: Improving Feature Selection of L2 Boosting by Correlation-Based Penalty Family

In high-dimensional setting, componentwise L₂boosting has been used to construct sparse model that performs well, but it tends to select many ineffective variables. Several sparse boosting methods, such as, SparseL₂Boosting and Twin Boosting, have been proposed to improve the variable selection of L₂boosting algorithm. In this article, we propose a new general sparse boosting method (GSBoosting). The relations are established between GSBoosting and other well known regularized variable selection methods in the orthogonal linear model, such as adaptive Lasso, hard thresholds, etc. Simulation results show that GSBoosting has good performance in both prediction and variable selection.