悲悯与救赎：女性性别自我指认的呐喊——王安忆、铁凝性爱小说合论

在新时期的文学中,王安忆、铁凝的几乎同时推出的性爱小说,首次将女性意识由过去的搭载在启蒙话语里向女性自身生存体验位移.这为女性文学所面临的话语缺失和欲望空白提供了解决的可能.她们通过质询女性身份的迷失,演绎女性悲剧宿命轮回,来思索女性的性别境遇、女性的生命体验,为女人的性别本体复归而呐喊.她们的思索更多的从社会文化历史层面切入,其女性意识尚处于朦胧模糊的阶段,独立的女性话语还没有形成.但为以后女性文学的发展起了铺垫的作用.     

Stability in Center Day Care: Relations with Children's Well‐being and Problem Behavior in Day Care1

Mothers and primary professional caregivers of 186 children, aged 6–30 months, participated in this study in which a new measure for daily stability in center day care was developed, describing staffing, grouping, and program features. Relative contributions of infants’ daily experiences of care stability, quality of care, and mother's daily stress to the child's adjustment to day care were analyzed with hierarchical regressions. The child's adjustment was assessed with the Child Behavior Checklist ( Achenbach, 1997 ) and with the Leiden Inventory for the Child's Well‐being in Day Care. Children enrolled in fewer care arrangements showed less internalizing behavior and more well‐being in the day care setting. Children had higher well‐being scores when trusted caregivers were more available. Mother's family management stress was related to more internalizing problem behavior. Interactions between mother's daily stress and stability in care were found to affect the child's adjustment to day care.     

On a class of bivariate mixed Sarmanov distributions

Multivariate distributions are more and more used to model the dependence encountered in many fields. However, classical multivariate distributions can be restrictive by their nature, while Sarmanov's multivariate distribution, by joining different marginals in a flexible and tractable dependence structure, often provides a valuable alternative. In this paper, we introduce some bivariate mixed Sarmanov distributions with the purpose to extend the class of bivariate Sarmanov distributions and to obtain new dependency structures. Special attention is paid to the bivariate mixed Sarmanov distribution with Poisson marginals and, in particular, to the resulting bivariate Sarmanov distributions with negative binomial and with Poisson‐inverse Gaussian marginals; these particular types of mixed distributions have possible applications in, for example modelling bivariate count data. The extension to higher dimensions is also discussed. Moreover, concerning the dependency structure, we also present some correlation formulas.     

A folded model for compositional data analysis

A folded type model is developed for analysing compositional data. The proposed model involves an extension of the α‐transformation for compositional data and provides a new and flexible class of distributions for modelling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation. The model is validated through simulation studies and examples which illustrate that the proposed model performs better in terms of capturing the data structure, when compared to the popular logistic normal distribution, and can be advantageous over a similar model without folding.     

Estimation of group means in generalized linear mixed models

In this study, we investigate the concept of the mean response for a treatment group mean as well as its estimation and prediction for generalized linear models with a subject‐wise random effect. Generalized linear models are commonly used to analyze categorical data. The model‐based mean for a treatment group usually estimates the response at the mean covariate. However, the mean response for the treatment group for studied population is at least equally important in the context of clinical trials. New methods were proposed to estimate such a mean response in generalized linear models; however, this has only been done when there are no random effects in the model. We suggest that, in a generalized linear mixed model (GLMM), there are at least two possible definitions of a treatment group mean response that can serve as estimation/prediction targets. The estimation of these treatment group means is important for healthcare professionals to be able to understand the absolute benefit vs risk. For both of these treatment group means, we propose a new set of methods that suggests how to estimate/predict both of them in a GLMMs with a univariate subject‐wise random effect. Our methods also suggest an easy way of constructing corresponding confidence and prediction intervals for both possible treatment group means. Simulations show that proposed confidence and prediction intervals provide correct empirical coverage probability under most circumstances. Proposed methods have also been applied to analyze hypoglycemia data from diabetes clinical trials.     

TITE‐BOIN‐ET: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding based on both efficacy and toxicity outcomes

One of the primary purposes of an oncology dose‐finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de‐escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late‐onset outcomes. To solve these issues, we propose the time‐to‐event Bayesian optimal interval design to accelerate dose‐finding based on cumulative and pending data of both efficacy and toxicity. The new design, named “TITE‐BOIN‐ET” design, is nonparametric and a model‐assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose‐finding trials compared with the model‐based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE‐BOIN‐ET design has advantages compared with the model‐based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE‐BOIN‐ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose‐finding trials.     

Understanding the Self Through Siblings: Self‐awareness Mediates the Sibling Effect on Social Understanding

This study addresses the role of sibling influence on social understanding in the second year of life, in a sample of families living in New Zealand who identify as Pacific Island (N = 43). We tested toddlers at 20 and 26 months on social understanding tasks, as well as their levels of self‐awareness on the Stipek self‐concept questionnaire. We hypothesized that the presence of siblings provides a rich resource from which to learn about the mind. There were significant differences in children's social understanding and self‐awareness as a function of having older siblings. Further analyses revealed that the relation between older siblings and social understanding was mediated by toddlers' level of self‐awareness.     

Peach Trees Grow in the Spaces Between People: Counselling Practices in the Territory of Violence

In this paper the authors reflect on how therapeutic practices wove a community of care and concern with a young man in South Africa who was at risk of anger ruling his life. A counselor and outsider witnesses participated in responding to the problems the young man and his family faced. The authors discuss the steps they took in the therapeutic journey and describe how their personal and professional lives were shaped by this collaboration. This work inspires and sustains their enthusiasm in seeking to understand how to best respond when positioned to counsel.     

Immigrant Children: Their Experience of Violence at School and Community in Host Country

This article describes our qualitative sociological study of immigrant children's life experiences of violence. We conducted interviews with 42 first‐generation immigrant children from any country, aged 9–13 years old, living in the Quebec City region (Canada). Results from three main themes are presented: representations of violence and concrete violent acts experienced; perceived effects of violence on children health and well‐being; and reactions and coping strategies. Overall, the narratives show that they may experience racist peer violence in school that leads to suffering situations, and they consequently have to develop strategies to maintain their well‐being. Social implications are discussed.     

Label‐ and Level‐Invariant Graphical Log‐Linear Models

We introduce two types of graphical log‐linear models: label‐ and level‐invariant models for triangle‐free graphs. These models generalise symmetry concepts in graphical log‐linear models and provide a tool with which to model symmetry in the discrete case. A label‐invariant model is category‐invariant and is preserved after permuting some of the vertices according to transformations that maintain the graph, whereas a level‐invariant model equates expected frequencies according to a given set of permutations. These new models can both be seen as instances of a new type of graphical log‐linear model termed the restricted graphical log‐linear model, or RGLL, in which equality restrictions on subsets of main effects and first‐order interactions are imposed. Their likelihood equations and graphical representation can be obtained from those derived for the RGLL models.