系统功能语言学语篇体裁研究

语篇体裁是系统功能语言学研究的重要内容。文章以Halliday&Hasan的语境理论、语域模型和Martin等人的语篇体裁理论为主线,梳理系统功能语言学对语篇体裁研究的发展过程和不同视角。      

系统功能语言学语域研究

语域是系统功能语言学语境理论的重要组成部分,对语域的研究也一直是功能语言学和语篇分析的研究热点。文章在回顾语域概念的内涵、特点及其功能的基础上,论述系统功能语言学内部对语域这个概念的不同解读,并介绍语域研究的一些新发展。      

考虑复杂关联情境的城市典型生命线运行风险因素识别方法研究

城市典型生命线以供热、电力、燃气系统为代表,具有公共性强、风险性高、关联性显著的特点。研究城市典型生命线运行风险因素识别问题对于相关部门防范风险具有重要意义,但现实中由多系统、多风险因素以及各类关联形成的复杂关联情境增加了解决该问题的难度。为此,本文首先构建了具有层级网络结构的风险因素识别框架,然后提出了一种考虑复杂关联情境的风险因素识别方法,将各专家针对系统关联和风险因素关联给出的语言评价信息转化为二元语义,并将决策试验与评价实验室（DEMATEL）法和Two-Additive Choquet（TAC）积分算子扩展到二元语义环境,进而实现各类关联的综合集成,从而确定风险因素的排序和归类,便于决策者研判风险根源、明晰风险因素本质。最后,以北京某样区为例验证了所提方法的潜在应用价值,并根据识别结果制定了针对性的风险防范策略,能够为相关部门联调联动开展风险防范提供决策支持。     

从边缘到主流：集体行动框架与文化情境

基于一个动员框架发生显著变化的社会运动案例,本文分析了主导运动前后两个阶段的动员框架及其转变机制。研究发现,框架转变源于社会运动领袖的策略性选择,转变背后体现的是动员框架与运动所处的宏观文化情境之间的微妙联系。具体而言,第一阶段的动员框架使用了文化情境中的边缘价值,动员效果并不理想;第二阶段,社会运动领袖对动员框架进行策略性的调整,将其与文化情境中的主流价值靠拢,在动员取得突破之后,又将边缘价值带回动员框架。随着运动的扩大,这些边缘价值被广泛传播与推广,并在运动之后延续下来,重塑了文化情境。     

试释日常会话中省略的语用意义

文章从认知理论的角度出发,借助关联理论框架,探讨了日常会话中省略的语用阐释。     

从关联理论看翻译

本文主要将语用学理论引入翻译这一学科当中,并指出关联理论对翻译的指导作用。首先介绍了传统意义上的语境观,并从关联理论出发,通过阐述明示推理模式,解释了翻译的本质。在翻译过程中,译者的任务是在交际中谋求原文作者、译者与译文读者之间的最佳关联,从而达到交际成功,完成其翻译任务。并指出关联理论中动态语境观对于翻译在文化差异及历史变化中的制约作用。     

歧义格式"我也V不好"

"我也V不好"是汉语中可以构成6种意思的歧义结构.这一歧义结构在句法结构、预设、语义指向、变换式、语音等方面有差别.进入这一结构的动词在语义特征上有一定限制.造成这一结构产生多种歧义的原因有有三个一是句法结构的多义性,二是词的多义性,三是语境.     

先验性的终结与消费社会的美学建构

奠基于形而上学的传统美学理论面对进入消费社会以来的审美经验已经无力作出有效的回应,而在先验终结、特别是语言学转向之后盛行的符号学方法在拒斥形而上学的同时隐蔽地抽空了生活经验,成为有内在矛盾的新整体主义思想;广义现象学和存在论提供了非先验的超越可能性,但必须接受不断涌现的新经验的检验以保证其作为知识的严格性而不是变成新的形而上学。要理解当代正在发生的审美经验,需要回到历史与生活情境,发现当代的新感性并以此作为当代美学建构的基础。     

Bayesian semi-parametric analysis of Poisson change-point regression models: application to policy-making in Cali,Colombia

A Poisson regression model with an offset assumes a constant baseline rate after accounting for measured covariates, which may lead to biased estimates of coefficients in an inhomogeneous Poisson process. To correctly estimate the effect of time-dependent covariates, we propose a Poisson change-point regression model with an offset that allows a time-varying baseline rate. When the non-constant pattern of a log baseline rate is modeled with a non-parametric step function, the resulting semi-parametric model involves a model component of varying dimensions and thus requires a sophisticated varying-dimensional inference to obtain the correct estimates of model parameters of a fixed dimension. To fit the proposed varying-dimensional model, we devise a state-of-the-art Markov chain Monte Carlo-type algorithm based on partial collapse. The proposed model and methods are used to investigate the association between the daily homicide rates in Cali, Colombia, and the policies that restrict the hours during which the legal sale of alcoholic beverages is permitted. While simultaneously identifying the latent changes in the baseline homicide rate which correspond to the incidence of sociopolitical events, we explore the effect of policies governing the sale of alcohol on homicide rates and seek a policy that balances the economic and cultural dependencies on alcohol sales to the health of the public.     

Analysis of Poisson varying-coefficient models with autoregression

In the regression analysis of time series of event counts, it is of interest to account for serial dependence that is likely to be present among such data as well as a nonlinear interaction between the expected event counts and predictors as a function of some underlying variables. We thus develop a Poisson autoregressive varying-coefficient model, which introduces autocorrelation through a latent process and allows regression coefficients to nonparametrically vary as a function of the underlying variables. The nonparametric functions for varying regression coefficients are estimated with data-driven basis selection, thereby avoiding overfitting and adapting to curvature variation. An efficient posterior sampling scheme is devised to analyse the proposed model. The proposed methodology is illustrated using simulated data and daily homicide data in Cali, Colombia.