Making autism through Ocean Heaven (海洋天堂, Haiyang tiantang) and the possibilities of realizing disability differently

This article focuses on a filmic portrayal of issues relating to autism in a Chinese context, using this as a prism through which to consider how meanings associated with disability in general, and the autistic spectrum in particular, are produced. Instead of perpetuating a reductive positive/negative approach to representation, the article focuses on the fluid and dynamic ways in which author and audience co-produce texts relating to disability in ways which are invariably influenced by, and reproducing of, specific socio-cultural and discursive circumstances. Meaning-making in relation to disability is, however, ongoing and there is potential to realize disability differently.     

“Mining is like a search and destroy mission”: The case of Silver City

ABSTRACT

While mines are often thought of as a driving force for development, this may not be the case due to power discrepancies between companies and local communities, as well as lack of protection provided by the government. In response, communities have begun to challenge mining companies. The industry itself has countered by developing negotiation techniques to gain local support. This study contributes to the literature on mining and ecosocial work practice, by drawing on the case of Silver City Nevada, where the broad consensus in the community is that the positive local-level impact of mining activities is negligible.     

基于文本挖掘的我国绿色金融政策研究

以我国2017—2019年绿色金融相关政策为数据源,通过提取政策文件的文本－行特征词和高频关键词,构建词云图、共现矩阵,采用高频词社会网络分析等方法,实现对这三年绿色金融相关政策内容信息的量化分析,从政策制定侧重点及政策内容上对文本进行总结。结果表明,我国2017—2019年绿色金融政策体现了政府对绿色金融企业机构激励机制及监管制度的完善,体现出这三年绿色金融发展的侧重点。     

汉语增量语词的强调标记化

基于语用和语言的互动关联,语用上的强调可能会渗入语言而成为语言自身的构成部分,语言会用某些显性语法形式标志被强调成分并使其范畴化。意义制约着汉语语词的历时流向,其主要表现之一是有相同或相似语义特征的语词往往会呈现为相同或相似的演变趋势。汉语的增量语词有通过语法隐喻、从其他认知域投射到言域中演变为强调标记的可能。这可通过增量语词的基本类型,如持续量语词、延及类语词、复加类语词的某些演变范例给以说明及解释。汉语的虚化成分并不单纯,从增量语词演变来的诸强调标记既有作用上的相似性,也有价值上的对立性,造成对立的主要原因是诸强调标记从它们的源词那里继承了某些特征,并且可能演变出了新特征。     

Legal Control over Big Data Criminal Investigation

The rapid growth of big data technology has become a major trend affecting the pattern of world development. Big data criminal investigation is a new type of criminal detection used extensively in the course of police practice at home and abroad. Its emergence indicates a trend in criminal justice towards ensuring security at the expense of privacy and exchanging rights for information. Big data criminal investigation highlights the backwardness and dysfunction of the traditional framework of legal norms, evident in doubts about the legal attributes of such investigation and the obvious limitations of techniques for distinguishing data content from metadata. This leaves a vacuum in the regulation of investigative power at the preliminary stage of investigation. Big data criminal investigation itself is a double-edge sword; in order to forestall the possible abuses it may entail in terms of deep and broad interventions in basic civil rights, big data criminal investigation should be brought under the necessary legal control. We therefore propose adopting a dual regulatory approach comprising investigative and data norms, selectively adopting the traditional normative framework of the principle of legality and the principle of proportionality, and at the same time supplementing it with other legal principles and mechanisms concerning the protection of personal information and data.     

论《白鹿原》中的传统文化元素

《白鹿原》真实地还原了20世纪中国国民的生存状态,挖掘了民族传统文化的多重元素,如儒家仁义与忠孝文化、宗法家族文化、农耕文化、传宗接代文化、男权文化与女性贞洁文化、民间神秘文化、土匪文化、传统民俗文化等,正面描写了仍存活于民间生活状态中的传统价值体系,塑造了具有儒家文化人格魅力的白嘉轩形象,也揭示了传统文化对人性的种种压抑与迫害等负面因素。     

On the multiple frontiers of extraction: excavating contemporary capitalism

Understanding the intensification and expansion of extractive industries in contemporary capitalism requires an approach attentive not only to the literal forms of extraction prevalent in mining and agribusiness but also to new fronts of extraction emerging in activities such as data mining and biocapitalism. This article introduces the concept of operations of capital to trace connections between the expansive logic of extraction and capitalist activity in the domains of logistics and finance. Arguing that extractive operations are at large across these domains, we explore their relevance for capital’s relation with its multiple outsides. The resulting analysis provides a basis for mapping struggles against the changing forms of dispossession and exploitation enabled by extraction.     

Climate,agriculture, and hunger: statistical prediction of undernourishment using nonlinear regression and data-mining techniques

An estimated 1 billion people suffer from hunger worldwide, and climate change, urbanization, and globalization have the potential to exacerbate this situation. Improved models for predicting food security are needed to understand these impacts and design interventions. However, food insecurity is the result of complex interactions between physical and socio-economic factors that can overwhelm linear regression models. More sophisticated data-mining approaches could provide an effective way to model these relationships and accurately predict food insecure situations. In this paper, we compare multiple regression and data-mining methods in their ability to predict the percent of a country's population that suffers from undernourishment using widely available predictor variables related to socio-economic settings, agricultural production and trade, and climate conditions. Averaging predictions from multiple models results in the lowest predictive error and provides an accurate method to predict undernourishment levels. Partial dependence plots are used to evaluate covariate influence and demonstrate the relationship between food insecurity and climatic and socio-economic variables. By providing insights into these relationships and a mechanism for predicting undernourishment using readily available data, statistical models like those developed here could be a useful tool for those tasked with understanding and addressing food insecurity.     

Data Mining and Knowledge Discovery in Databases – An Overview

Data mining seeks to extract useful, but previously unknown, information from typically massive collections of non-experimental, sometimes non-traditional data. From the perspective of statisticians, this paper surveys techniques used and contributions from fields such as data warehousing, machine learning from artificial intelligence, and visualization as well as statistics. It concludes that statistical thinking and design of analysis, as exemplified by achievements in clinical epidemiology, may fit well with the emerging activities of data mining and 'knowledge discovery in databases' (DM&KDD).     

大数据背景下网络借贷的信用风险评估——以人人贷为例

在大数据时代,网贷平台每天流动着海量交易数据。为充分利用这些数据控制信用风险,运用数据挖掘算法建立了信用风险评估模型。由于网贷数据多为非平衡数据,所以通过多次尝试使用SMOTE算法进行处理,提高了模型评估性能。研究发现:随机森林模型更适合用于信用风险评估,其次是CART、ANN、C4.5。用户的婚姻、房/车产(贷)等信息重要程度较低,而公司规模、工作时间等信息,历史借款、信用评分等信用档案信息在信用风险评估中尤为重要。