高校学生群体性事件的预警与危机干预研究

伴随着我国社会经济发展、社会转型、社会关系多元化和教育改革,各种类型的高校学生群体性事件屡屡发生,对高校教学环境带来严重影响,甚至波及社会的和谐稳定。因此,要采取行之有效的应对措施。本文在分析了高校学生群体性事件新特征的基础上,从学生群体性事件危机管理的长效机制、网络舆情和校园文化三个维度进行探索,以便应对新形势下的高校学生群体性事件。     

新媒体环境下体育赛事传播发展分析

信息技术的不断发展,促使网络、手机、数字电视等新媒体飞速变革,对体育赛事的传播和发展产生了深远的影响,渗透到社会生活的各个领域,促进体育赛事向市场化、产业化和全球化发展。新媒体对体育赛事传播的影响在更深远的层面继续进行,并且不断地延伸。文章通过对在新媒体环境下体育赛事传播的现状、前景的具体分析和概括,力求为体育赛事的发展和与新媒体的进一步融合寻求针对性的技术路径。     

悲剧意识与文化传统

人类发展和人类社会形态更替过程都包含了悲剧性。只有人们正确地认识和把握现实的悲剧性,并想改变自己的生存困境,悲剧意识才可能形成。悲剧意识是指在人类发展过程和社会发展进程中,体现在某些人物身上的那种“无为而为”的心理倾向,即某些人明知他们将来的所作所为一定会失败(实际上也会失败),却仍然锐意去追求、奋斗、牺牲的心理趋势。它由两个部分构成:一、人类生存和社会发展的困境;二、人类对人类生存和社会发展困境的超越、应战的精神。西方文化决定了它的悲剧意识表现形态,西方人的悲剧意识表现得深刻、彻底,中国文化决定了它的悲剧意识表现形态,中国人悲剧意识表现得浮泛、留有余地。     

独生子女大学生运动项目特点研究

以独生子女大学生运动项目特点为研究对象 ,通过对一～四年级 2 5 38名在校独生子女大学生进行了问卷调查和个别访问 ,并对其调查结果进行数据统计分析研究 ,提出了改进目前普通高校传统体育课教学方法 ,及早调整课程设置 ,按运动项目特点安排课程内容 ,为培养独生子女大学生终生参加体育锻炼打下良好基础     

群体性突发事件中政府机会主义行为的演化博弈分析

群体性突发事件成为影响当前我国社会稳定和实现现代化平稳过渡的重要因素,但是研究主要是基于实践经验的概述和总结分析上,缺少合适的理论分析工具揭示群体性突发事件的演化规律和发展特征。本文尝试运用演化博弈理论建立了地方政府部门与社会弱势群体之间的演化博弈模型,分析了地方政府部门机会主义行为导致的群体性突发事件呈现扩大化的趋势。研究结果表明:(1)如果社会弱势群体的情绪和行动尚未激化,至多停留在集体上访或集会等体制内平和的诉求手段,机会主义的地方政府将采取高压强硬策略;此时博弈均衡结果是(妥协或平和诉求,强硬);(2)一旦弱势群体中出现少数分子采取了"打砸抢"等暴力抗争的行动,由于机会主义政府转而做出较大让步,从而诱导其他弱势成员相继采取暴力抗争行动,事态反而进一步呈现扩大趋势。因此,群体性突发事件处置过程中,地方政府的机会主义行为不仅延误事件处置的最佳时机,而且直接导致了事态的扩大化。最后结合数值分析和瓮安事件案例,剖析了地方政府部门采取的机会主义应急处置措施,以及本文理论模型的适用范围。     

On the Olympic Games: An Afterword

The article discusses some of the issues that sociologists research with respect to the Olympic Games.     

基于应对重大突发事件的人口信息化管理分析

根据我国现行人口信息化管理的现状 ,结合应对重大突发事件的实际需要 ,系统分析了人口基础信息建设管理与应对重大突发事件关联的信息平台间的层次关系 ,在基于对流动人口信息化管理现行模式的基础之上 ,提出了完善人口信息化管理及建设模型     

新任教师反思能力的培养策略——基于个人生活史视角

教师反思能力主要是指教师个体对自身教育观念及行为的认知、监控、调节能力,是教师从事教育教学工作,提升课堂教学有效性、教育研究能力以及彰显教师主体性的重要能力之一,在新任教师专业成长初期应引起足够的重视。教师生活史是关于教师个体教育生活的历史,具有生成性、广泛性、体验性和缄默性。教师反思能力的形成与教师个人生活史联系紧密,其生活中的学习经历、"重要他人"、关键事件以及实践经验等都作为一种隐性知识影响新任教师反思能力的形成。为了提升新任教师的反思能力、促进其专业发展,新任教师应高度关注个人生活史,关注日常生活世界,以批判的视角审视生活史,走出"熟知陷阱";"回望关键,抓住关键,注重关键事件中的核心要素;重视他人引导,向同事请教,吸取优秀教师特别是教学名师的成长经验;勤于学习,在理论学习与教学实践中养成反思的习惯,不断提升反思能力,增强教学的有效性。     

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.     

The attributable estimand: A new approach to account for intercurrent events

The term “intercurrent events” has recently been used to describe events in clinical trials that may complicate the definition and calculation of the treatment effect estimand. This paper focuses on the use of an attributable estimand to address intercurrent events. Those events that are considered to be adversely related to randomized treatment (eg, discontinuation due to adverse events or lack of efficacy) are considered attributable and handled with a composite estimand strategy, while a hypothetical estimand strategy is used for intercurrent events not considered to be related to randomized treatment (eg, unrelated adverse events). We explore several options for how to implement this approach and compare them to hypothetical “efficacy” and treatment policy estimand strategies through a series of simulation studies whose design is inspired by recent trials in chronic obstructive pulmonary disease (COPD), and we illustrate through an analysis of a recently completed COPD trial.