文理科大学生英语词汇记忆策略培训研究

基于O′Malley&Chamot的词汇记忆策略分类,以显性和课堂融入相结合的方式对114名福建农林大学2008级文理科大学生进行为期一学年的英语词汇记忆策略培训,旨在研究策略培训效果并对比文理科大学生词汇记忆策略培训效果的差异。研究结果表明:英语词汇记忆策略培训能提高文理科大学生的策略意识,为他们提供更多有效地记忆词汇的方法,扩大他们记忆策略的选择范围,提高他们的词汇水平。策略培训后想象策略和分类策略仍然不常被使用。文理科大学生在反复策略、联想策略、构词策略、分类策略、上下文记忆策略以及词汇水平提高程度上都存在显著差异,其诱因是不同的认知风格和学习动机。     

团队反省理论研究述评

团队反省(team reflexivity)的概念构思基于对创新任务团队作业过程的考察而提出.有关团队反省的维度划分及测量尚未达成共识.已有研究中涉及的影响团队反省的前因可归为个体特征、任务特征和团队特征三大类;而团队反省的结果主要表现为对团队效能、效率以及创新的影响.此外,团队反省还在结果依赖性、意见不一致及其在与团队产出变量间的作用关系中起调节作用.文章在对该构思相关研究进行梳理的基础上,对其未来的发展趋势进行了初步探讨.     

英汉语中的重叠修辞功效之比较

在英汉语中,为了表达的需要,有时有意识地重复使用某个词素、音节、词汇、短语甚或句子,却能使行文达到极好的修辞效果,这种语言现象,在英汉两种语言中被分别冠之以repetition和“复叠”的修辞格名称。从语言分析的各个层面对两种语言中的这种重叠现象加以分析比较,概括总结了其构成方式的异同及其运用所产生的修辞效果。     

一种考虑基差非对称影响的期货波动性预测模型研究——基于上海铜期货市场的实证分析

研究了基差对上海铜期货收益波动率影响的非对称效应。实证结果表明,基差对铜期货的收益波动存在显著的非对称影响,其中负基差对波动性的影响要明显大于正的基差项。通过与GARCH 模型和未考虑基差项的SEGARCH模型对铜期货的样本外预测能力的比较表明,考虑基差对波动性的非对称影响的AE-GARCH模型能显著减小铜期货波动性预测的误差。     

负语用迁移对外语学习和跨文化交际的正面影响

负语用迁移一直以来都被认为是造成语用失误的首要因素 ,大量研究集中在如何克服负语用迁移对外语学习和跨文化交际的负面影响 ,而忽略了它的正面作用。因此 ,外语教师和学习者把负语用迁移当成外语学习中的障碍 ,而没有充分利用它的正面作用来加快目的语的习得。本文着重探讨负语用迁移对外语学习和跨文化交际的正面影响 ,并藉此提出几点对外语教学的看法。     

同轴波导FEL放大器的非线性分析

以同轴波导、同轴混合磁铁摇摆器为模型,导出了自洽的注波三维非线性方程组,在考虑了电子初始速度零散效应、空间电荷波效应的影响及电子注在互作用区入口处的绝热压缩过程的基础上,编制了相应的计算软件。同时,通过数值分析的方法研究了自由电子激光器的饱和效率、频带宽度等高频特性和变参数摇摆器结构对延缓饱和、提高效率的作用。     

中国加入WTO正，负效应的重新阐释

我国加入WTO有利也有弊.其正效应主要表现在增加我国在对外贸易谈判中的主动权,促进制度创新与观念更新,重塑我国企业的造新精神,迅速改变我国服务贸易领域的经营面貌.其短期内的负效应表现在可能降低外汇储备水平,使企业经营、就业和证券行业陷入困境.     

Alternative estimation methods for identifying contagion effects in dynamic social networks: A latent-space adjusted approach

Contagion effects, also known as peer effects or social influence process, have become more and more central to social science, especially with the availability of longitudinal social network data. However, contagion effects are usually difficult to identify, as they are often entangled with other factors, such as homophily in the selection process, the individual’s preference for the same social settings, etc. Methods currently available either do not solve these problems or require strong assumptions. Following Shalizi and Thomas (2011), I frame this difficulty as an omitted variable bias problem, and I propose several alternative estimation methods that have potentials to correctly identify contagion effects when there is an unobserved trait that co-determines the influence and the selection. The Monte-Carlo simulation results suggest that a latent-space adjusted estimator is especially promising. It outperforms other estimators that are traditionally used to deal with the unobserved variables, including a structural equation based estimator and an instrumental variable estimator.     

Racial diversity, minority concentration, and trust in Canadian urban neighborhoods

Using a sample of 42,329 respondents nested within 4254 Canadian urban neighborhoods, this study demonstrates the conceptual and empirical importance of making a distinction between neighborhood racial diversity and minority concentration, and examines how each is uniquely associated with trust. Our analysis shows that at a given level of racial minority concentration, Whites are more trusting when their minority neighbors are more evenly distributed across racial minority groups. Meanwhile, Whites are less trusting as the neighborhood share of racial minorities increases. Overall, the effect of racial minority concentration tends to prevail over that of racial diversity.     

Deconvolution methods for non-parametric inference in two-level mixed models

Summary.  We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency.