文化自觉：照亮中国基础教育理性化阴影之灯

中国传统基础教育以私塾为形式,人文性的四书五经为其内容。清末民初,私塾为理性化的班级授课制所取代,这是理性在教育领域的第一次胜利;近十年来,基础教育集中于乡镇以上学校,村小学被拆并,乡镇初中日渐缩减,农村教育资源被抽空,这是理性在教育领域的进一步扩张。理性化带有形式合理而实质不合理的悖论特质。基础教育之理性化带来高效益的同时,也导致文化意识与价值观念越来越淡漠。目前,较为富强的中国社会也越发意识到,文化归宿之于中国人安身立命以及社会的可持续发展的价值所在。克服教育理性化阴影,在于文化的再植入。故而,面对教育理性化,亟待嵌入文化意识,须将本土文化嵌入基础教育之中以避免教育的无根性。     

I'm Not a Bad Mother — Stories of Mothering‐on‐the‐Edge

This article is located in the field of custody and day‐to‐day care, with a focus on experiences of mothering where mothers no longer have everyday care of their children. At the time of writing, tens of thousands of mothers live separately from their children in Aotearoa, New Zealand. However, there is a general perception of rarity associated with non‐resident mothers, partly due to their reluctance to speak of loss of custody. In this article, we develop a theoretical discussion around the inside/outside relationships between non‐resident mothers and their children that informs therapeutic practice. We argue that mothers who live apart from their children are situated at the edge or periphery of their children's day‐to‐day lives and experience this as outside the lives of their children. Nevertheless, mothers' stories speak of qualities of (m)othering that have been born of and nurtured by longstanding and long‐term commitment to their children.     

Optimum covariate designs in partially balanced incomplete block (PBIB) design set-ups

The use of covariates in block designs is necessary when the covariates cannot be controlled like the blocking factor in the experiment. In this paper, we consider the situation where there is some flexibility for selection in the values of the covariates. The choice of values of the covariates for a given block design attaining minimum variance for estimation of each of the parameters has attracted attention in recent times. Optimum covariate designs in simple set-ups such as completely randomised design (CRD), randomised block design (RBD) and some series of balanced incomplete block design (BIBD) have already been considered. In this paper, optimum covariate designs have been considered for the more complex set-ups of different partially balanced incomplete block (PBIB) designs, which are popular among practitioners. The optimum covariate designs depend much on the methods of construction of the basic PBIB designs. Different combinatorial arrangements and tools such as orthogonal arrays, Hadamard matrices and different kinds of products of matrices viz. Khatri–Rao product, Kronecker product have been conveniently used to construct optimum covariate designs with as many covariates as possible.     

Inference of non-centrality parameter of a truncated non-central chi-squared distribution

Non-central chi-squared distribution plays a vital role in statistical testing procedures. Estimation of the non-centrality parameter provides valuable information for the power calculation of the associated test. We are interested in the statistical inference property of the non-centrality parameter estimate based on one observation (usually a summary statistic) from a truncated chi-squared distribution. This work is motivated by the application of the flexible two-stage design in case–control studies, where the sample size needed for the second stage of a two-stage study can be determined adaptively by the results of the first stage. We first study the moment estimate for the truncated distribution and prove its existence, uniqueness, and inadmissibility and convergence properties. We then define a new class of estimates that includes the moment estimate as a special case. Among this class of estimates, we recommend to use one member that outperforms the moment estimate in a wide range of scenarios. We also present two methods for constructing confidence intervals. Simulation studies are conducted to evaluate the performance of the proposed point and interval estimates.     

Classification rules for triply multivariate data with an AR(1) correlation structure on the repeated measures over time

In this article we study the problem of classification of three-level multivariate data, where multiple q$q$-variate observations are measured on u$u$-sites and over p$p$-time points, under the assumption of multivariate normality. The new classification rules with certain structured and unstructured mean vectors and covariance structures are very efficient in small sample scenario, when the number of observations is not adequate to estimate the unknown variance–covariance matrix. These classification rules successfully model the correlation structure on successive repeated measurements over time. Computation algorithms for maximum likelihood estimates of the unknown population parameters are presented. Simulation results show that the introduction of sites in the classification rules improves their performance over the existing classification rules without the sites.     

Characterizations based on order statistics under sampling without replacement

In this paper, we consider finite populations and investigate their characterizations by regressions of order statistics under sampling without replacement. We also investigate some asymptotic results when the size of the population goes to infinity.     

On the stability of the risk hull method for projection estimators

We consider in this paper the regularization by projection of a linear inverse problem Y=Af+εξ$Y=\mathit{Af}+\epsilon \xi$ where ξ$\xi$ denotes a Gaussian white noise, A   a compact operator and ε>0$\epsilon >0$ a noise level. Compared to the standard unbiased risk estimation (URE) method, the risk hull minimization (RHM) procedure presents a very interesting numerical behavior. However, the regularization in the singular value decomposition setting requires the knowledge of the eigenvalues of A$A$. Here, we deal with noisy eigenvalues: only observations on this sequence are available. We study the efficiency of the RHM method in this situation. More generally, we shed light on some properties usually related to the regularization with a noisy operator.     

Semiparametric estimation for count data through weighted distributions

This paper is concerned with semiparametric discrete kernel estimators when the unknown count distribution can be considered to have a general weighted Poisson form. The estimator is constructed by multiplying the Poisson estimate with a nonparametric discrete kernel-type estimate of the Poisson weight function. Comparisons are then carried out with the ordinary discrete kernel probability mass function estimators. The Poisson weight function is thus a local multiplicative correction factor, and is considered as the uniform measure to detect departures from the equidispersed Poisson distribution. In this way, the effects of dispersion and zero-proportion with respect to the standard Poisson distribution are also minimized. This method of estimation is also applied to the weighted binomial form for the count distribution having a finite support. The proposed estimators, in addition to being simple, easy-to-implement and effective, also outperform the competing nonparametric and parametric estimators in finite-sample situations. Two examples illustrate this new semiparametric estimation.