Pluralism and Universality of Environmental Discourse: The Dilemma Between Damage Caused by the Japanese Macaque and its Protection

Abstract:  The subject of this paper is to describe the complexity of relations between man and wild monkeys, which causes difficulties in forming a social consensus related to their conservation and management. To present how the relations are in everyday life, and how they are simplified in universal discursive spaces, interviews and a questionnaire survey have been conducted. As a result, it is clarified that there are three kinds of discursive spaces, that of politics, natural science, and residents. People who share the land with wild monkeys have various terms and stories about their relations with monkeys. The monkeys have various values in the discourse of residents, including many troubles. The existence of monkeys is vivid and close, and can be described as "accessible" nature. However, in discussion of politics or natural sciences, there is less diversity in monkeys' values. In these discursive spaces, monkeys are described simply in the framework of "distant" nature, and wild monkey issues are constructed as a "pest animal issue". The simplified protocol between accessible and distant nature makes the wild monkey issues simple. And the attitudes of residents are shown as controversial through the contradiction between simple-complex issues. In conclusion, this contradiction cannot be solved only by filling the gap between accessible and distant nature with social consensus, but also by connecting the two and enhancing the dynamism of society as a whole.     

数字空间正义的理论内涵、缺失表象与弥合路径

数字空间是互联网集团代表的市场资本和掌握数字知识的技术精英共同塑造的新型社会空间,而数字空间正义就是对数字空间生产过程中出现的一系列空间异化现象的反思和批判,意指社会个体能平等公平地分配数字资源以及拥有充分数字权利。数字空间生产中,受市场资本和技术精英的共同支配和影响,数字空间日益沦为数字资本攫取剩余价值的空间,技术精英进行监视以及自我约束的规训空间,类型和功能均具明显单向度特征的同质空间,延续并再造新型社会不平等的区隔空间,进行免费劳动的剥削空间以及引导预售的消费空间。长此以往,数字社会日益沦为单向度社会,数字生活沦为单向度生活,人也日益丧失否定、批判和创新而成为单向度的人。为打造正义的数字空间,实现诗意的数字生活,须坚持数字空间生产的人本性和正义性取向,通过完善契合数字空间运行和发展的法律法规和伦理道德,规范数字资本时空运作以及生产复合型数字空间,再造专业型数字文化空间激发数字生活的集体记忆,秉承差异再造原则并保障主体数字权利,确保数字技术开发中价值理性和工具理性的平衡等路径予以实现。     

营造“包容性空间”:国际化社区的治理策略研究

营造包容性空间,构建包容性社会,加快推进空间治理的现代化进程,是现代化治理的重要目标。基于江苏省南京市Q社区的空间营造案例,从社区空间营造策略的视角出发,剖析国际化社区如何营造包容性空间。研究发现,基层政府基于中国居民与外籍人士的空间服务需求,运用多元参与、互动协商、文化治理等软性手段,通过建设人与居住地、社会、自然相融合的表征的包容性空间,组织温度、服务与文化协同相融的包容性空间的实践,构筑开放、包容与共享的包容性空间的表征等方式营造包容性空间。在营造包容性空间的过程中,基层政府空间营造行动的关键策略是采用“分型管理、内育外引”的主体包容策略、“内外兼修、空间包容”的技术包容策略、“多元网格、协商自治”的制度包容策略及“制度共融、成果共享”的结果包容策略,其根本目标在于打造国际化社区的治理共同体。包容性空间的治理技术对优化基层政府的空间治理能力、提升居民的参与水平与再造国际化社区秩序有积极效应,但亦潜藏多主体协同失序、政社关系掣肘、族群关系区隔、文化不稳定及包容性制度偏差等失灵风险。因此,基层政府需厚植多主体合作的治理机制、经营包容性空间的“适度性”,以利于国际化社区善治目标的实现。     

云南少数民族民居灵物的人类学研究

本文运用人类学理论方法,对在云南少数民族民居建筑中普遍存在、内容多元、形式丰富、极富功利色彩的民居灵物进行与之有结构性关联的少数民族宇宙观、宗教仪式禁忌和空间象征几个范畴的解读,以阐释民居灵物所蕴含的丰富的地方性知识和其背后的文化意义.并时与之相关的中国少数民族文化遗产保护的现状与策略提出个人观点.     

空间的焦虑与释放——新力量导演创作的一种观察

近年来“中国电影新力量”导演声势渐显,他们关注社会问题,常取用当代都市作为空间情境,并以对空间的观察反映当下的现实与焦虑。在他们的电影创作中,空间有着典型的现代症候,如空间中人与人之间的疏离,空间中时间的紊乱,以及空间的功能性失位。这些空间的焦虑是某种社会心理发生改变的症候,而通过影像对现实的针砭与对边缘群体的关怀则是新力量导演们释放焦虑的方法。     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

Forecasting Multivariate Road Traffic Flows Using Bayesian Dynamic Graphical Models,Splines and Other Traffic Variables

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple model to use. While statistical flow forecasting models usually base their forecasts on flow data alone, data for other traffic variables are also routinely collected. This paper shows how cubic splines can be used to incorporate extra variables into the LMDM in order to enhance flow forecasts. Cubic splines are also introduced into the LMDM to parsimoniously accommodate the daily cycle exhibited by traffic flows. The proposed methodology allows the LMDM to provide more accurate forecasts when forecasting flows in a real high‐dimensional traffic data set. The resulting extended LMDM can deal with some important traffic modelling issues not usually considered in flow forecasting models. Additionally, the model can be implemented in a real‐time environment, a crucial requirement for traffic management systems designed to support decisions and actions to alleviate congestion and keep traffic flowing.     

Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces

In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problems, and the magnitude of their effects tapers off. It is reasonable to model the number of relevant features as a diverging sequence when sample size increases. In this paper, we investigate the properties of the extended Bayes information criterion (EBIC) (Chen and Chen, 2008) for feature selection in linear regression models with diverging number of relevant features in high or ultra-high dimensional feature spaces. The selection consistency of the EBIC in this situation is established. The application of EBIC to feature selection is considered in a SCAD cum EBIC procedure. Simulation studies are conducted to demonstrate the performance of the SCAD cum EBIC procedure in finite sample cases.     

Asymptotically minimax non–parametric function estimation with positivity constraints I

The spatially inhomogeneous smoothness of the non-parametric density or regression-function to be estimated by non-parametric methods is often modelled by Besov- and Triebel-type smoothness constraints. For such problems, Donoho and Johnstone [D.L. Donoho and I.M. Johnstone, Minimax estimation via wavelet shrinkage. Ann. Stat. 26 (1998), pp. 879–921.], Delyon and Juditsky [B. Delyon and A. Juditsky, On minimax wavelet estimators, Appl. Comput. Harmon. Anal. 3 (1996), pp. 215–228.] studied minimax rates of convergence for wavelet estimators with thresholding, while Lepski et al. [O.V. Lepski, E. Mammen, and V.G. Spokoiny, Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimators with variable bandwidth selectors, Ann. Stat. 25 (1997), pp. 929–947.] proposed a variable bandwidth selection for kernel estimators that achieved optimal rates over Besov classes. However, a second challenge in many real applications of non-parametric curve estimation is that the function must be positive. Here, we show how to construct estimators under positivity constraints that satisfy these constraints and also achieve minimax rates over the appropriate smoothness class.