大数据背景下个人信息处理行为的法律规制——以个人信息处理行为的双重外部性为分析视角

大数据背景下个人信息处理行为既引发数据安全风险等负外部性，也会带来分享经济价值实现等正外部性，"大数据悖论"现象揭示了双重外部性成因。基于"外部性内在化"原理赋予信息主体以个人信息权并赋予数据控制者以大数据财产权虽有助分别规制其双重外部性，但网络大数据背景下其双重外部性规制彼此交互影响而面临两难困境。法经济学关于"损害之相互性"理论证立了基于"风险导向理念"规制个人信息处理以破解其两难困境的经济逻辑。大数据产业发展有赖个人信息处理的"外部性外部化"而实现分享经济。法经济学关于"公地喜剧"理论证立了基于"外部性外部化"规制个人信息处理而满足大数据产业发展需要的经济逻辑。大数据背景下个人信息处理的规制有赖从"压制型法"到兼及"回应型法"的理念转换，从"外部性内在化"兼及"外部性外部化"的机制并举，从数据资源权利配置兼及数字技术权力干预的措施协同，实现大数据产业创新与个人信息安全的有机平衡。     

基于大数据合作资产的适应性创新--数字经济的创新逻辑(二)

万物互联时代,数据被认为是与土地、劳动、资本、知识、技术和管理并重的第七大生产要素。当前,多样化的商业实践创新对数据价值的深度挖掘正在突破经济学在传统意义上对数据及其价值的限定,然而,理论上对此还缺乏相应的概念凝练和探讨。本文从企业与消费者价值共创角度提出大数据合作资产的概念,并探讨基于大数据合作资产和适应性创新的数字经济创新逻辑。研究表明:大数据合作资产是一种互动性资源,是企业与消费者在数字化服务交互中成为能够被另一方所拥有和利用并能创造当前或未来经济收益的数字化资产;单纯拥有异质资源不一定会形成合作资产,只有当异质化资源被有效整合、且被使用于企业与消费者服务交换中,才能体现为行动者创造收益的价值潜力;协同演化促进了企业与消费者对彼此的适应性调整,基于大数据合作资产激发以即时调整、即时反馈和难以预测为特征的适应性创新,进而促进了数字经济创新。     

基于交叉效率和合作博弈的决策单元排序方法

在数据包络分析中，大量的交叉效率模型已被提出。然而选择不同的目标模型将实现不一样的交叉效率评价。本文基于针对单个决策单元实施的对抗型和仁慈型两个交叉效率模型，用合作博弈方法来研究交叉效率模型的选取，并利用Shapley值对决策单元进行排序。最后通过实例分析显示该排序方法充分利用了最小交叉效率和最大交叉效率的信息完全排序了所有决策单元，具有一定的综合性和合理性。     

大数据时代项目评审中社会伦理问题的对抗性设计

通过对抗性设计方法解决项目评审中的社会伦理问题，为专家、申请者、大众提供争论和表达异议的民主环境。项目自动评价平台完成民主环境的构建，呈现专家、申请者、平台的争胜性关系，揭示霸权，旨在重塑自由、平等、公平、正义、特权等的项目评审中的社会伦理。     

生产性服务业对出口贸易的影响研究

生产性服务业将人力资本和知识资本导入生产过程促进了制造业升级,其发展程度代表着一个国家或地区的现代化程度。充分有效利用服务业资源已成为工业企业提高竞争力进而扩大出口贸易的必然选择。从专业化分工、技术创新等方面探讨生产性服务业对出口贸易的影响机理,结合辽宁省生产性服务业发展状况建立面板数据模型,对其是否影响辽宁出口贸易进行实证分析,结果证实辽宁生产性服务业发展促进了出口贸易发展,交通运输、仓储及邮政和商务服务业对辽宁出口贸易的影响较为明显。据此提出发展生产性服务业以促进辽宁贸易的建议。     

New options for national population surveys: The implications of internet and smartphone coverage

Challenges to survey data collection have increased the costs of social research via face-to-face surveys so much that it may become extremely difficult for social scientists to continue using these methods. A key drawback to less expensive Internet-based alternatives is the threat of biased results from coverage errors in survey data. The rise of Internet-enabled smartphones presents an opportunity to re-examine the issue of Internet coverage for surveys and its implications for coverage bias. Two questions (on Internet access and smartphone ownership) were added to the National Survey of Family Growth (NSFG), a U.S. national probability survey of women and men age 15–44, using a continuous sample design. We examine 16 quarters (4 years) of data, from September 2012 to August 2016.Overall, we estimate that 82.9% of the target NSFG population has Internet access, and 81.6% has a smartphone. Combined, this means that about 90.7% of U.S. residents age 15–44 have Internet access, via either traditional devices or a smartphone. We find some evidence of compensatory coverage when looking at key race/ethnicity and age subgroups. For instance, while Black teens (15–18) have the lowest estimated rate of Internet access (81.9%) and the lowest rate of smartphone usage (72.6%), an estimated 88.0% of this subgroup has some form of Internet access.We also examine the socio-demographic correlates of Internet and smartphone coverage, separately and combined, as indicators of technology access in this population. In addition, we look at the effect of differential coverage on key estimates produced by the NSFG, related to fertility, family formation, and sexual activity. While this does not address nonresponse or measurement biases that may differ for alternative modes, our paper has implications for possible coverage biases that may arise when switching to a Web-based mode of data collection, either for follow-up surveys or to replace the main face-to-face data collection.     

Clustering transformed compositional data using K-means,with applications in gene expression and bicycle sharing system data

Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e. data whose rows belong to the simplex) remains largely unexplored in cases where the observed value is equal or close to zero for one or more samples. This work is motivated by the analysis of two applications, both focused on the categorization of compositional profiles: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental conditions; and (2) finding patterns in the usage of stations over the course of one week in the Velib' bicycle sharing system in Paris, France. For both of these applications, we make use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension called the Log Centered Log Ratio, in conjunction with the K-means algorithm. We use a non-asymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq, on both the gene expression and bicycle sharing system data.     

The Semantic Web and the BIBFRAME Initiative

The implementation of the semantic web based on linked data principles represents a movement toward transforming the World Wide Web into a database of linked resources, where data may be widely reused and shared. Web services can be enhanced by drawing on semantically aware data made available by a variety of providers. The Bibliographic Framework (BIBFRAME) Initiative seeks to expose rich library metadata to the semantic web, configured so that semantic data from other sources can be incorporated in meeting library user needs.     

Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science

ABSTRACT

Scientific research of all kinds should be guided by statistical thinking: in the design and conduct of the study, in the disciplined exploration and enlightened display of the data, and to avoid statistical pitfalls in the interpretation of the results. However, formal, probability-based statistical inference should play no role in most scientific research, which is inherently exploratory, requiring flexible methods of analysis that inherently risk overfitting. The nature of exploratory work is that data are used to help guide model choice, and under these circumstances, uncertainty cannot be precisely quantified, because of the inevitable model selection bias that results. To be valid, statistical inference should be restricted to situations where the study design and analysis plan are specified prior to data collection. Exploratory data analysis provides the flexibility needed for most other situations, including statistical methods that are regularized, robust, or nonparametric. Of course, no individual statistical analysis should be considered sufficient to establish scientific validity: research requires many sets of data along many lines of evidence, with a watchfulness for systematic error. Replicating and predicting findings in new data and new settings is a stronger way of validating claims than blessing results from an isolated study with statistical inferences.