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.     

上市公司存货计量方法选择与决策相关性分析

通过分析新存货准则中引入的存货计量属性,即历史成本计量、公允价值计量、可变现净值和未来现金流量的现值的本质特征,分析了其与决策的相关性,并通过对我国部分上市公司存货计价方法选择的深层分析,揭示了决策相关性在存货计价方法应用中所传递的信息和表达的目的。     

《广雅》释义方法探析

《广雅》的释义方法主要有声训和义训.其声训是对汉代声训的继承和延续;其义训对后世辞书编撰和注释家提供了十分宝贵的经验,产生了广泛的影响.     

实现学生工作手段的现代化 提高德育工作的实效性

在贯彻落实党中央16号文件精神的过程中,首都师范大学运用各种管理系统,自主开发学生管理系统,加强网络建设,逐步实现工作的网络化.通过手段创新,提高了学生工作的效率,提高了德育工作的实效性和时效性.     

高校理想信念教育状况及实效性路径思考——以西南科技大学学生为调查样本

增强高校大学生理想信念教育的实效性是学校思想政治教育的一项重要工作.通过对西南科技大学学生的调查发现,当前我国高校大学生的理想信念主流是好的,但部分学生也不同程度存在政治信仰迷茫、价值取向扭曲、社会责任感缺乏、团结协作观念较差等问题,这与高校理想信念教育的效果不理想关系极大.     

研究儿童特性 提高训练质量

面临我国目前独生子女多的国情,为使我国足球事业后继有人,足球教练面临的课题是如何深入研究儿童生理、心理特点,合理安排训练方式、内容,以提高训练质量。     

Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes.     

Reasons That Could Lead People to Divorce in an Evolutionary Perspective: Evidence from Cyprus

In the current research, we aimed to identify the reasons that could drive people to get a divorce. In Study 1, we employed qualitative research methods, and we identified 62 reasons that could potentially drive people to get a divorce. Using quantitative research methods, in Study 2 we classified these reasons into 7 broader factors. We found that being a harmful spouse was the most important factor for divorce, followed by incompatibility and in-law problems. We also found significant sex differences in several factors such as women indicating a higher willingness than men to divorce a harmful spouse. In addition, participants with children were less willing to divorce a harmful spouse than those who had no children. Finally, using second-order principal components analysis, we classified the 7 factors into 2 broader domains of reasons for getting a divorce.