Toward a Hybrid Model for Effective Organizing in Communities of Color

This paper examines the feminist and Alinsky approaches to community organizing, both of which have had great impact on the field of organizing. Viewing the two models as flexible and interflowing, the authors then propose a hybrid organizing model that draws from the strengths of both approaches. The model also calls for continual assessment of the changing organizing context and flexibility to change the blend of strategies according to the needs of the moment. Finally, the paper looks at how the dynamics of race and ethnicity affect organizing in communites of color, and discusses the usefulness of the proposed model in dealing with these dynamics.     

OPTIMAL IV ESTIMATION OF SYSTEMS WITH STOCHASTIC REGRESSORS AND VAR DISTURBANCES WITH APPLICATIONS TO DYNAMIC SYSTEMS

This paper considers the general problem of Feasible Generalized Least Squares Instrumental Variables (FGLS IV) estimation using optimal instruments. First we summarize the sufficient conditions for the FGLS IV estimator to be asymptotically equivalent to an optimal GLS IV estimator. Then we specialize to stationary dynamic systems with stationary VAR errors, and use the sufficient conditions to derive new moment conditions for these models. These moment conditions produce useful IVs from the lagged endogenous variables, despite the correlation between errors and endogenous variables. This use of the information contained in the lagged endogenous variables expands the class of IV estimators under consideration and thereby potentially improves both asymptotic and small-sample efficiency of the optimal IV estimator in the class. Some Monte Carlo experiments compare the new methods with those of Hatanaka (1976). For the DGP used in the Monte Carlo experiments, asymptotic efficiency is strictly improved by the new IVs, and experimental small-sample efficiency is improved as well.     

Autoregressiv model identification for multivariate time series

A procedure is developed for the identification of autoregressive models for stationary invertible multivariate Gaussian time series. Model selection is based on either the AIC information criterion or on a statistic called CVR, cross-validatory residual sum of squares. An example is given to show that the forecasts generated by these models compare favorably with those generated by other common time series modeling techniques.     

On probit versus logit dynamic mixed models for binary panel data

In this paper, we consider inferences in a binary dynamic mixed model. The existing estimation approaches mainly estimate the regression effects and the dynamic dependence parameters either through the estimation of the random effects or by avoiding the random effects technically. Under the assumption that the random effects follow a Gaussian distribution, we propose a generalized quasilikelihood (GQL) approach for the estimation of the parameters of the dynamic mixed models. The proposed approach is computationally less cumbersome than the exact maximum likelihood (ML) approach. We also carry out the GQL estimation under two competitive, namely, probit and logit mixed models, and discuss both the asymptotic and small-sample behaviour of their estimators.     

Maximum Likelihood Estimation for Multinomial‐Poisson Models: A Generalization of Birch's Numerical Invariance Results

Abstract. This study gives a generalization of Birch's log‐linear model numerical invariance result. The generalization is given in the form of a sufficient condition for numerical invariance that is simple to verify in practice and is applicable for a much broader class of models than log‐linear models. Unlike Birch's log‐linear result, the generalization herein does not rely on any relationship between sufficient statistics and maximum likelihood estimates. Indeed the generalization does not rely on the existence of a reduced set of sufficient statistics. Instead, the concept of homogeneity takes centre stage. Several examples illustrate the utility of non‐log‐linear models, the invariance (and non‐invariance) of fitted values, and the invariance (and non‐invariance) of certain approximating distributions.     

Selection of regress or va riables when E(Y) is an unknown honlinear function

We consider the problem of deciding which of a set of p independent variables x₁ X₂J x_s we are to regard as being functionally involved in the mean of a dependent normal random variable Y and estimating E( Y) in terms of the chosen x's. This mean is an unknown function (assumed to be doubly differentiable) of some or all of the x's, so that the problem is of wide relevance. We approximate to the hypersurface in two different ways, and select within each approximation:

(a)For the situation where the mean of Y is assumed to be a linear function of the x's, we use ono of the optimum methods of selection.

(b)More generally, in the space of the X's the function will be approximately linear in a relatively small region. Accordingly this p-dimensional space is subdivided into smaller regions by a clustering procedure, and a hyperplane if fitted with in each region to aproximate to the unknown responce surface.An adaption of an optimum-regressor-selection procedure is then used to assist in the selection of the regressors

Approximate F tests are given to choose between models, including deciding how many x's to retain. Alternatively: the application of Akaike's Extended Maximum Likelihood Principle provides another way of choosing between the models and of selecting regressor variables. The methods are applied to data on glass manufacture.     

社会工作模式介入高校学生管理的理论路径

中国高校学生管理引入社会工作模式的研究起步比西方晚,相关的理论研究在2000年以后逐步拓展。但对于这个领域的研究,却还比较零星,对于现有文献的梳理更是不足。针对现有学校社会工作的相关研究,从高校管理传统模式的弊端、社会工作模式与高校学生管理的契合、社会工作模式的角色定位及其运用模式等方面进行归纳、评述,不难发现学术界对高校社会工作的研究对象并不十分明确,相关研究只注重解决问题而忽略了预防问题,对高校学生管理引入社会工作模式的研究尚停留在理论层面,实证研究还需进一步加强。     

认知、语言交际与译者心智模型

翻译的过程是人类所经历并体验的复杂心智过程,涉及到社会文化、心理认知、情绪取向和意识状态等众多因素。从心智模型的视角来研究翻译过程,是认知科学的研究成果“心智模型”应用于翻译研究的一个全新且有生命力的尝试。翻译过程中的意义传送必定通过译者心智模型密切地关联和过滤。译者的心智模型在很大程度上助益或影响了译者对于原作语言形式的体验及其意义的表征推理以及其在目的语中意义及语言的选择和表述,从而决定了译文的语言风格及意义色彩。     

Two-stage DEA models with undesirable input-intermediate-outputs

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). Many studies have examined DEA efficiencies of two-stage systems, where all the outputs from the first stage are the only inputs to the second stage. Although single-stage DEA models with undesirable input-outputs have been extensively studied, there still lacks of more systematical investigation on two-stage DEA with undesirable variables. For instance, depending on its operating model, even whether an intermediate variable is desirable or undesirable can be questionable for a particular two-stage system. Furthermore, most of the existing studies on two-stage systems focus on the case where only the final outputs are undesirable. In this work, we try to systematically examine two-stage DEA models with undesirable input-intermediate-outputs. Particularly, we utilize the free-disposal axioms to construct the production possibility sets (PPS) and the corresponding DEA models with undesirable variables. The proposed models are then used to illustrate some theoretical perspectives by using the data of China׳s listed banks.