美国早期融合教育师资在线培养经验及启示

“办好公平而有质量的教育”是我国当前教育发展的主题,融合教育作为沟通普通教育和特殊教育的桥梁,是新时期 基础教育扩优提质计划的重要组成部分,亦是确保学前教育普及普惠的应有之义,其发展的关键在于培养高质量的融合 教育师资。通过对美国西南地区某研究型公立大学开发的一套早期融合教育师资在线培养课程进行解读,进而探究我国 学前融合教育教师培养路径。首先,从教学形式、教育对象和课程目标三方面分析该在线培养课程与同类型的线下课程 之间的差异：其次,总结该早期融合教育师资在线培养课程的特点;最后,结合该早期融合教育师资在线培养课程建设案 例,从教育对象、教育过程、课程评价和社会影响四方面,就创新我国学前融合教育人才培养方式的问题形成相应启示与 建议。     

健全粮食安全利益保障体系的机制设计与政策建议

发挥经济激励对国家粮食安全的保障作用至关重要。经过多年来的实践探索,我国基本建立起多维立体的涉粮激励政策体系,形成了稳定粮农收入、优化营商环境、调动产区积极性和保障消费者可得性的政策框架,但仍存在种粮农民利润空间压缩、粮油加工业后劲潜力不足、粮食主产区高产穷县等问题。针对粮食安全利益保障水平较低背后成因,探索构建以实现“产得出、运得到、买得起”为目标、协调粮食产业链利益相关主体关系、充分释放经济效益在保障粮食安全中作用的机制,并提出健全粮食安全利益保障体系的对策建议:一是优化补贴政策,强化政策保本功能;二是加快发展农业社会化服务,实现经营增效;三是协调产业链主体关系;四是逐步健全主产区利益补偿机制;五是健全应急储备及市场调节机制。     

数字金融对农户气候适应性行为的影响

气候适应性技术采用率低下已成为制约农业可持续发展的重要因素,数字金融可能影响农户的气候适应性行为。基于河南、陕西、山西三省1 384份农户微观调查数据,运用内生转换回归模型构建反事实分析框架,实证分析数字金融使用对农户气候适应性行为的影响效应及其作用机制。研究发现:使用数字金融能显著促进农户气候适应性技术采纳行为,具体表现为在反事实假设下,使用数字金融的农户若未使用其气候适应性技术采纳程度将下降;未使用数字金融的农户如果使用了,其气候适应性技术采纳程度将上升。机制分析表明,数字金融能够提高借贷易得性与信息易得性,进而促进农户采纳气候适应性行为,农户对于金融包容性的认知能够正向增强数字金融对农户气候适应性行为的影响效应。异质性分析表明,数字金融使用对于资本型适应性行为影响的边际效应最大,在反事实假设下也表现为数字金融使用对农户资本型适应性行为提升效果最强;数字金融对农业收入占家庭收入比率较高、接受过培训的农户采纳气候适应性技术促进效应更高。     

考虑组织成本的两阶段竞赛机制研究

在企业招聘实践中,招聘成本是企业管理者在制定招聘策略时所考虑的重要因素之一。为保证招聘效果并控制成本,企业管理者将选择不同成本的人才测评方法,分阶段地筛选应聘者,选择测评方法和确定各阶段应聘者人数成为企业管理者主要的决策问题。在此管理背景下,进行两阶段竞赛机制的研究,并将节约组织成本作为竞赛设计者目标的一部分。分别修改竞赛者总努力最大化和最高努力最大化这两个传统目标,得到两个修正后的优化目标。当竞赛者能力分布函数为幂函数时,通过模型分析和数值模拟。研究结果表明,两个优化目标下的最优竞赛机制具有共同性质,即针对确定的奖励选择竞赛筛选方法时,单位组织成本不能过大;在确定筛选方法后,如果奖励与单位组织成本的比值过小,或低能力者占竞赛者的大部分,那么应仅允许两名竞赛者进入复试。另外,对于以竞赛者最高努力最大化为目标的竞赛,如果高能力者相对较多时,第二阶段的最优竞赛人数也为2。     

Decision settings analysis – a tool for analysis and design of human activity systems

The paper describes a methodology to be used for analysis and design of human activity systems. The methodology is based on an analysis of the decision settings whereas most other decision analysis methodologies are analysing the process. The decision concept is analysed and discussed. A distinction between programmed and programmable as well as non-programmed and non-programmable decisions is proposed. A classification of different information types for decision making is presented. A methodology based on a systemic and systematic analysis of the information requirements of an organization is proposed. This methodology also indicates organizational discrepancies and information imbalances. The methodology focuses the settings of the decisions on all levels of organizations. The methodology can be regarded as a dynamic, learning system. The author proposes further research on the individuals decision making abilities.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

Bootstrapping regression models with BLUS residuals

To bootstrap a regression problem, pairs of response and explanatory variables or residuals can be resam‐pled, according to whether we believe that the explanatory variables are random or fixed. In the latter case, different residuals have been proposed in the literature, including the ordinary residuals (Efron 1979), standardized residuals (Bickel & Freedman 1983) and Studentized residuals (Weber 1984). Freedman (1981) has shown that the bootstrap from ordinary residuals is asymptotically valid when the number of cases increases and the number of variables is fixed. Bickel & Freedman (1983) have shown the asymptotic validity for ordinary residuals when the number of variables and the number of cases both increase, provided that the ratio of the two converges to zero at an appropriate rate. In this paper, the authors introduce the use of BLUS (Best Linear Unbiased with Scalar covariance matrix) residuals in bootstrapping regression models. The main advantage of the BLUS residuals, introduced in Theil (1965), is that they are uncorrelated. The main disadvantage is that only n —p residuals can be computed for a regression problem with n cases and p variables. The asymptotic results of Freedman (1981) and Bickel & Freedman (1983) for the ordinary (and standardized) residuals are generalized to the BLUS residuals. A small simulation study shows that even though only n — p residuals are available, in small samples bootstrapping BLUS residuals can be as good as, and sometimes better than, bootstrapping from standardized or Studentized residuals.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Differential equation models for statistical functions

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation models of data, and smoothing over complicated multidimensional domains. This paper aims to stimulate interest in this approach to functional estimation problems, rather than provide carefully worked out methods.