Life Satisfaction,Self-Esteem,and Loneliness Among LGB Adults and Heterosexual Adults in China

ABSTRACT

Low levels of life satisfaction have been linked to low self-esteem and loneliness, but this association has never been tested directly in LGB (lesbian/gay/bisexual) populations. We compared 275 Chinese LGB adults to 275 demographic-matched Chinese heterosexual controls on life satisfaction, self-esteem, and loneliness. LGB adults reported lower levels of self-esteem and higher levels of loneliness than heterosexuals, but similar levels of overall life satisfaction. Self-esteem partially mediated (but did not moderate) the relationship between loneliness and life satisfaction in both groups. Hierarchical regressions indicated that demographic variables, loneliness, and self-esteem can predict life satisfaction in both LGB and heterosexual adults, but explained more variance of life satisfaction in the LGB group. Thus self-esteem and loneliness play a more important role in life satisfaction for LGB rather than heterosexual Chinese adults.     

穆旦与奥登：历史追问与现实反思——兼与江弱水先生商榷

江弱水在《外国文学评论》上发表的《伪奥登风与非中国性：重估穆旦》指出穆旦诗歌是对奥登诗歌的移译，而非中国性这一特征造成了穆旦诗歌的失败。而文本从选择奥登的原因、伪奥登风的消解、穆旦诗歌中的“非中国性”追问、传统与现代的反思等四个方面探讨和重新考察穆旦诗歌与奥登的关联，进而认为穆旦诗歌的特质与奥登并非密切相关。     

Sample size re‐estimation incorporating prior information on a nuisance parameter

Prior information is often incorporated informally when planning a clinical trial. Here, we present an approach on how to incorporate prior information, such as data from historical clinical trials, into the nuisance parameter–based sample size re‐estimation in a design with an internal pilot study. We focus on trials with continuous endpoints in which the outcome variance is the nuisance parameter. For planning and analyzing the trial, frequentist methods are considered. Moreover, the external information on the variance is summarized by the Bayesian meta‐analytic‐predictive approach. To incorporate external information into the sample size re‐estimation, we propose to update the meta‐analytic‐predictive prior based on the results of the internal pilot study and to re‐estimate the sample size using an estimator from the posterior. By means of a simulation study, we compare the operating characteristics such as power and sample size distribution of the proposed procedure with the traditional sample size re‐estimation approach that uses the pooled variance estimator. The simulation study shows that, if no prior‐data conflict is present, incorporating external information into the sample size re‐estimation improves the operating characteristics compared to the traditional approach. In the case of a prior‐data conflict, that is, when the variance of the ongoing clinical trial is unequal to the prior location, the performance of the traditional sample size re‐estimation procedure is in general superior, even when the prior information is robustified. When considering to include prior information in sample size re‐estimation, the potential gains should be balanced against the risks.     

汉语双字词常模特征跨词性对比研究

汉语中不同词性词有不同常模特征规律。名词的具体性、意象性、语义透明度最高，但笔画数最少；动词的词频最高、习得年龄最晚,但感官经验度最低；形容词的熟悉性、主观词频和情感经验度最高，但具体性最低。同时，具体性、意象性和感官经验度之间的相互关联只存在于名词和形容词中。13个常模特征可以归为频率、语义、视觉复杂性和词义数四个公共因子，但各因子所主要代表的变量情况在不同词性词之间有相似，亦有不同。另外，动词和形容词之间的相似性高于它们分别与名词之间的相似性程度，习得年龄、情感经验度、意象性和主观词义数最能把名词与另外两种词性区分开来。     

论胡乔木的新体咏物诗

胡乔木的新体咏物诗不仅数量多，而且质量高，是其新体诗的精华所在，有其独特的艺术个性和鲜明的艺术特征，诸如由物起兴、物显人情；托物抒怀、物人一体；借物述理、生动形象等等，极具艺术魅力，为新体诗的发展作出了重要的艺术贡献。     

女性在田野调查中的性别处境研究

当研究者进入田野，田野对象就会试图将研究者放置在一个位置上，而研究者也会给自身寻找一个适合的位置．田野对象对研究者的定位反映的即是一般的社会性别价值。通过对12位女性研究者进行访谈，拟了解女性研究者是否因自身性别而遭受困境，其性别先前理解又是如何影响她对于性别差别遭遇的反应的。结果发现，虽然处于不同的年龄、专业领域，拥有不同的生活阅历，女性研究者在田野中的经验却有着相似之处。即她们的专业角色被忽视，更多地被视为传统女性的角色。在这一现实背景下，为了使研究能够顺利进行。女性研究者通常的选择是表现得有礼貌或者不得不忍气吞声。     

赫斯和青年马克思的人道思想对比

赫斯和马克思是生长在同一个时代和社会背景下有着相近思想兴趣的两个人,因为都有着对当时社会中人类苦难的特别关注与极度怜悯,而有了寻找苦难根源及寻求改变当时人类苦难之良方的志向,他们一生都在为人类的解放而奋斗;可以说"人道问题意识"是他们心头共同的用一生的行动探寻和思索的问题意识。如果再追问他们是如何探寻"人道问题"的,则发现他们一直在试图克服"受苦者的目光",即试图解决人的个体存在与类存在之间的矛盾。     

Model averaging for treatment effect estimation in subgroups

In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting “random high” / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early‐phase clinical trial.