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。     

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.     

Iwi community justice panels reduce harm from re-offending

ABSTRACT

The effect of iwi community justice panels is assessed using data from seven years of implementation. Panel participants of 1013 are compared to a control group drawn from 73,000 offenders charged with similar offences and propensity-matched by age, gender, ethnicity, offending history and location. The dependent measure is re-harm from offending calculated using the NZ Crime Harm Index. Panel participants did not reduce their rate of offending compared to matched controls. However, there is a significant reduction in harm from post-panel offending, on average by 26.9 ± 21.05 equivalised prison days. The relative reduction is 22.25% of the harm committed by matched controls. Iwi panels are an effective alternative justice resolution. Panel participants are found to re-offend with minor offences and thus any focus on rates of offending would miss the complexity of the restorative justice process that reduces the harm of re-offending.     

试述图表资料在生物教学中的作用

图表资料具有直观性、系统性及概括性的特点,在生物教学中有着重要地位及作用．     

On approximate likelihood inference in a poisson mixed model

A two-step estimation approach is proposed for the fixed-effect parameters, random effects and their variance σ² of a Poisson mixed model. In the first step, it is proposed to construct a small σ²-based approximate likelihood function of the data and utilize this function to estimate the fixed-effect parameters and σ². In the second step, the random effects are estimated by minimizing their posterior mean squared error. Methods of Waclawiw and Liang (1993) based on so-called Stein-type estimating functions and of Breslow and Clayton (1993) based on penalized quasilikelihood are compared with the proposed likelihood method. The results of a simulation study on the performance of all three approaches are reported.     

Bias Reduction using Stochastic Approximation

The paper studies stochastic approximation as a technique for bias reduction. The proposed method does not require approximating the bias explicitly, nor does it rely on having independent identically distributed (i.i.d.) data. The method always removes the leading bias term, under very mild conditions, as long as auxiliary samples from distributions with given parameters are available. Expectation and variance of the bias-corrected estimate are given. Examples in sequential clinical trials (non-i.i.d. case), curved exponential models (i.i.d. case) and length-biased sampling (where the estimates are inconsistent) are used to illustrate the applications of the proposed method and its small sample properties.     

大数据在流动人口服务中的应用

大数据是一种宝贵的资源,流动人口大数据正成为发现问题、把握规律、科学决策和精准管理的重要依据。流动人口大数据具有数据量大、来源广泛、层级复杂、特色鲜明、缺乏共享等特点。流动人口大数据在服务管理中可以发挥现状分析、趋势预测、决策支持、精准服务、绩效评价、智能匹配等作用。为使流动人口大数据更好地服务流动人口,应整合数据资源、促进数据开放共享,搭建流动人口数据平台,采用新技术开展流动人口动态监测并利用多种方法对流动人口大数据进行分析。     

国内网络素养领域的研究现状——基于CNKI数据库的文献计量分析

以中国知网CNKI期刊数据库为数据来源,采用文献计量方法对国内网络素养的研究现状进行分析发现：目前国内网络素养研究的文献增长符合指数增长规律,正处于知识积累的发展阶段;该研究领域为多学科交叉领域,教育学、马克思主义学、新闻学与传播学是其研究的核心阵地;目前核心作者群还比较薄弱,固定的科研合作团队较少;大学生及青少年（包括未成年人）是该领域的主要研究对象,新媒体、网络舆情、网络行为、网络理性、网络道德等是该领域的研究热点。     

大数据背景下的商业秘密保护

通过研究商业秘密与数据信息的关系、现行商业秘密保护模式的缺陷、以及大数据背景下商业秘密保护等问题,指出在大数据背景下商业秘密保护面临的困境,并从立法、行政监管与企业自身防范等方面提出相应的建议;以期完善与统一商业秘密立法,增加并完善数据化商业秘密立法保护,政府相关部门进行严格的行政监管,以及企业提高防范技术等措施,进而改善大数据背景下的商业秘密保护的现状,弥补立法的缺失,从而增强企业竞争力,促进市场有序健康发展.