系统法在课程教学改革中的应用研究

本文以系统方法为指导,就日语专业《高级日语》课从明确教学目标和要求、做好授课前的准备工作、注重课堂教学方法的针对性使用、开展有效的课外活动等四个环节试作论述。     

对高年级日语精读课的几点思考

高年级日语精读课是一门实践性、综合性很强的课程。文章就高年级日语课的特点、教学方法改革和教学手段更新等方面进行分析和论述。     

试论乐学教育

乐学是种愉快认知、学习的心态和行为,在从中国古代教育特别是西方教育学、心理学梳理的基础上,探讨了突出学生主体、培养乐学心理、实施乐学教育的途径和方法.     

高校采购信息公开化初探

高校采购信息公开是杜绝高校采购活动中暗箱操作,规范采购行为的重要举措,是高校采购满足高校教学、科研设备技术指标、性能指标要求的重要途径。从当前高校政府采购中存在的主要问题分析来看,设置专职机构,理顺管理体制,积极探索新技术,采取有效的公开形式,加强廉正监督,保证高校采购信息公开、公平、公正。     

文献研究中语言学方法的限度

学术研究中,语言学方法常常用于文献的断代研究,但是,这一方法是具有一定限度的。若干失误往往表现在方法论的预设,凡是某一时代文献没有记载的语汇就不属于该时代;且在语汇的时间层次、语言使用的差异性、语法的时间层次上皆有一定的盲区。认识这一现象,有助于我们在使用上述方法进行学术研究时确定其存在的限度。     

“无用”之大用——英语专业文学教育功能浅识

文学之用存在于它的"无用"之中,即在于大用,而非世俗之所谓急功近利之用。本文从文学与语言、文化素养及交际能力等要素间相互关系入手,就英语专业文学教育的功能及功能实现的方法作了初步的探讨。     

Exploring Parameter Relations for Multi‐Stage Models in Stage‐Wise Constant and Time Dependent Hazard Rates

Single cohort stage‐frequency data are considered when assessing the stage reached by individuals through destructive sampling. For this type of data, when all hazard rates are assumed constant and equal, Laplace transform methods have been applied in the past to estimate the parameters in each stage‐duration distribution and the overall hazard rates. If hazard rates are not all equal, estimating stage‐duration parameters using Laplace transform methods becomes complex. In this paper, two new models are proposed to estimate stage‐dependent maturation parameters using Laplace transform methods where non‐trivial hazard rates apply. The first model encompasses hazard rates that are constant within each stage but vary between stages. The second model encompasses time‐dependent hazard rates within stages. Moreover, this paper introduces a method for estimating the hazard rate in each stage for the stage‐wise constant hazard rates model. This work presents methods that could be used in specific types of laboratory studies, but the main motivation is to explore the relationships between stage maturation parameters that, in future work, could be exploited in applying Bayesian approaches. The application of the methodology in each model is evaluated using simulated data in order to illustrate the structure of these models.     

关于“非遗”保护三个基本问题的话语分析

话语研究不仅关心人们如何言说,而且更为重视分析决定言说方式的深层动因。“非遗”保护既是一个社会各阶层权力角逐的场域,又是一个可通过不断协商达成共识的平台。人们对于“非遗”保护的认识人言言殊的根本原因在于参与“非遗”保护的社会各阶层各自的利益诉求并不完全一致。“非遗”保护工作正是在争论中逐渐确立了自身的工作目标、指导原则、实施方案,体现了极强的话语建构性。“非遗”作为一种传统生活方式的隐形载体,活态传承性是其精髓与灵魂;“非遗”保护必须以人为本,只有让“非遗”与现实的人民群众的日常生活发生联系和耦合,“非遗”保护才可能取得实效。     

In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.