从强制收容到无偿救助——基于多源流理论的政策分析

政策科学是一门与社会实际紧密联系的学科,它随着人们对社会实际的认识的不断深入而发展。在孙志刚事件发生后,我们发现传统的政策过程阶段论无法很好地解释这一事件的发生以及从强制收容到无偿救助的转变过程。文章试图运用多源流理论来解释这一过程,从而提供一种不同于传统视角但同样合理的解释。     

社群互动与认同重构———以东太湖流域的一个移民社会为例

污名是移民在地化过程中常见的现象,但学界对于污名与认同的关系的认识仍有待深入。通过对一个位于吴江东太湖流域的移民社会的考察,发现移民在与本地人的污名互动中形成了多样化的认同分类,即原籍认同分类“河南人 - 苏北人 - 本地人”、地方认同分类“太湖人 - 高田人”以及污名分类“太湖强盗 - 高田蛮子”。 污名互动表面上制造了社会的分裂,但实际上强调了不同社群的共存关系,其对于移民完成在地化和认同重构具有重要意义。 从污名切入进行移民认同的研究时,应在污名互动中全面考察互动各方的污名规范与污名实践,从而解答充满污名的社会如何具备共同体的可能性的问题。     

论意象与联想力、想象力之互动——以“多”、“二”、“一（0）”螺旋结构切入作考察

联想力与想象力是思维力的两大翅膀,藉以让思维力遨游于客观与主观时空,以通贯心、物,上撤其本、下撤其末,将真实世界、伦理世界与艺术世界融通为一,以呈现真、善、美的圆融境域.而意与象即相当于心与物,为人类思维活动的原动力,自然和联想力与想象力关系密切.大体说来,就在联想力与想象力的作用下,意象得以形成、表现、组织与统合;其中意象之形成、表现,关涉到偏于主观的"联想力与想象力"所形成之形象思维;意象之组织,关涉到偏于客观的"联想力与想象力"所形成之逻辑思维;而意象之统合,则关涉到合客观与主观为一的"联想力与想象力"所形成之综合思维.由此可知意象与联想力、想象力,在思维力的大力牵合下,不但三位一体,而且使它们形成"意象←→联想力、想象力"的"多"、"二"、"一(0)"螺旋结构.     

Asymptotic distributions of quadratic forms occurring in changepoint problems

A stochastic-process approach is used to derive the asymptotic distributions of quadratic forms occurring in the analysis of changepoint data.     

Didn't we just see you? Time to recurrence among frequently encountered families in CPS

In child protection services, multiple maltreatment recurrences, or chronic maltreatment, has been a concern drawing increased attention because of its persistent harm to the children and the need to consider more effective intervention strategies to meet its unique needs. Timing has been an important issue in understanding the pattern of chronic maltreatment. No existing research has examined the influence of the interval between previous maltreatment incidents on future recurrences. The current study uses state administrative data to conduct longitudinal analyses to examine how the interval between previous maltreatment incidents is associated with the likelihood of future maltreatment occurrence among children who encountered multiple maltreatment recurrences. The findings suggest that short intervals are associated with increased likelihood of encountering a future recurrence, while controlling various covariates. The findings suggest the possibility of including the interval between previous maltreatment incidents as an indicator for child maltreatment risk assessment, and the need for developing responsive intervention strategies to stop the trend of chronic maltreatment.     

Alternative estimators for the population totals in multiple characteristic surveys

The problem of estimating the population totals of multiple characteristics using without replacement sampling design is addressed. The alternative estimators for the study characteristics which are unrelated or have the low correlations with the characteristic used in sample selection are suggested. The efficiency of the estimators under two super population models, approximating the situations in which both the characteristics are unrelated and also when they are related, is studed. A numerical investigation is also carried out to get insight into the performance of the estimators in real applications.     

Selection of multiple deferred (dependent) state sampling plans

This paper presents procedures and tables for the selection of minimum sample size multiple deferred (dependent) state sampling plan of type MDS and MDS - 1 for various entry conditions such as (AQL, LQL), (AQL, AOQL) etc.     

The Target Parameter of Adjusted R-Squared in Fixed-Design Experiments

R-squared (R²) and adjusted R-squared (R²_Adj) are sometimes viewed as statistics detached from any target parameter, and sometimes as estimators for the population multiple correlation. The latter interpretation is meaningful only if the explanatory variables are random. This article proposes an alternative perspective for the case where the x’s are fixed. A new parameter is defined, in a similar fashion to the construction of R², but relying on the true parameters rather than their estimates. (The parameter definition includes also the fixed x values.) This parameter is referred to as the “parametric” coefficient of determination, and denoted by ρ²_*. The proposed ρ²_* remains stable when irrelevant variables are removed (or added), unlike the unadjusted R², which always goes up when variables, either relevant or not, are added to the model (and goes down when they are removed). The value of the traditional R²_Adj may go up or down with added (or removed) variables, either relevant or not. It is shown that the unadjusted R² overestimates ρ²_*, while the traditional R²_Adj underestimates it. It is also shown that for simple linear regression the magnitude of the bias of R²_Adj can be as high as the bias of the unadjusted R² (while their signs are opposite). Asymptotic convergence in probability of R²_Adj to ρ²_* is demonstrated. The effects of model parameters on the bias of R² and R²_Adj are characterized analytically and numerically. An alternative bi-adjusted estimator is presented and evaluated.     

Imputation of repeatedly observed multinomial variables in longitudinal surveys

It is now a standard practice to replace missing data in longitudinal surveys with imputed values, but there is still much uncertainty about the best approach to adopt. Using data from a real survey, we compared different strategies combining multiple imputation and the chained equations method, the two main objectives being (1) to explore the impact of the explanatory variables in the chained regression equations and (2) to study the effect of imputation on causality between successive waves of the survey. Results were very stable from one simulation to another, and no systematic bias did appear. The critical points of the method lied in the proper choice of covariates and in the respect of the temporal relation between variables.