Data development for international research—Phase I: International conflict and national characteristics

In 1986 developers and users of quantitative data in international and comparative politics organized to maintain, extend, and develop major datasets and to provide a stronger basis for coordinating data‐related activities. National Science Foundation funding established for a three‐year period Data Development for International Research. Its first phase (DDIR‐I) focused on improving datasets on both national capabilities and international conflict; and generating renewed efforts to better the data collections on internation events and international political economy. DDIR‐I's work, now virtually complete, augmented the quantity and quality of this national data resource and set the stage for another NSF‐funded project on internation event data (DDIR‐II). A final report on DDIR‐II is forthcoming in summer 1994.     

Violence Against Civilians: A Disaggregated Analysis

Disaggregated approaches to conflict research have led to new insights into the patterns and processes of political violence in developing countries. This article uses the most comprehensive subnational political violence data (ACLED) to observe where and when violence against civilians occurs within civil wars. Several new conclusions are evident from an event-based analysis of civilian violence: retribution or collateral damage are poor explanations for attacks on the unarmed. Instead, civilians are targeted because they are accessible; rebel groups kill more civilians, often in an attempt to create new frontlines for conflict. However, governments are also responsible for high rates of civilian death, yet they often “contract” this violence out to militias. This analysis confirms that there are multiple violent groups within civil war spaces, and small opposition groups commit higher levels of violence against civilians in local spaces. The strength of a violent group compared to its competition shapes how much civilian violence it commits. The results suggest that theories that emphasize civilian support and retribution as a basis for violence against civilians have overlooked the importance of how multiple violent opposition groups compete within civil wars, and how civilians suffer as a result.     

Intraobserver Error in Macromorphoscopic Trait Data, ,

As part of a much larger investigation into the use of macromorphoscopic trait data by forensic anthropologists to estimate ancestry from unidentified skeletal remains, we conducted a fourteen‐year (2002–2016) intraobserver error study. Motivated by the development of a large macromorphoscopic database―which will potentially utilize data collected in 2002―quantification of observer error, the impact of technological improvements in macromorphoscopic trait data collection and observer experience is necessary. To maximize comparisons between the two samples, ten macromorphoscopic traits were assessed. Results revealed three patterns of error relating to observer experience, the introduction of new technologies, and error inherent in the method. Overall, this study found the effect of error on macromorphoscopic trait analysis could be predicted and did not significantly impact their utility.     

Police officer attitudes to the practicalities of the sex offenders’ register,ViSOR and Child Sexual Abuse Disclosure Scheme in England and Wales

This paper examines police officer understandings of and attitudes to the sex offenders’ register, Violent and Sex Offenders’ Register (ViSOR) and Child Sexual Offender Disclosure Scheme (CSODS) in England and Wales – an under-researched area in the management of sexual offenders in the UK. This research is an adaptation of an American study utilising a mixed-methods approach, combining an online questionnaire survey (N?=?227) with a series of semi-structured interviews (N?=?27). The study found that police officers, irrespective of role, were generally supportive of the register, ViSOR and CSODS in principle, but they thought that logistics, practicalities, infrastructure, multi-agency collaboration and public understandings had problematic impacts on the scheme in practice. The participants believed that greater investment was needed in terms of time and resource to make the register, ViSOR and CSODS easier to use and access and thus fit for purpose.     

Longitudinal Analysis of Links Between Bullying Victimization and Psychosomatic Maladjustment in Swedish Schoolchildren

Cross-sectional studies of bullying mask variability in categories of and persistence of bullying victimization. Longitudinal, individual-level data offers a greater insight into schoolchildren’s psychosomatic maladjustment as a consequence of bullying. Swedish schoolchildren (n = 3,349), with unique identifiers, in 44 schools (4th–9th grade), answered a questionnaire at baseline and 1-year follow-up. Longitudinal trends for nonvictims (88%), ceased victims (4.7%), new victims (5.7%), and continuing victims (1.6%) revealed that new victims had the largest decrease in well-being; continuing victims had a smaller though not significant decrease; while ceased victims showed a small, (nonsignificant) increase in well-being over the measurement period. It was also discovered that children not bullied at baseline but bullied subsequently, differed, at baseline, from their never-bullied peers through lower levels of overall well-being. It is argued that this finding has implications for prevention strategies.     

大数据，认知与计划经济——与何大安教授商榷

数据是明晰的,客观的,而信息本质上是主观的,隐含的和实践的,数据被个体理解之后才会转化成“他的”信息。人们在使用数据之前先会判断该数据的重要性,这种判断因人因时因地而异,而大数据本身并不能替个人做出这样的判断。信息分散在无数人的头脑中,不能被某个头脑或超级计算机集中掌握。个体确定目标后,才能决定把什么样的数据作为手段来实现自己的目标,否则他会在海量的数据面前不知所措。数据是人们行动的产物。作为“历史”,数据本身不能告诉人们未来他们应该做什么。另外,没有市场的运行,甚至数据本身都不可能产生。正确的进路应该是“让个体的知识得以充分利用”,而不是何大安教授提出的“改善个体的认知”。何大安教授预设的“智慧大脑”类似于计划经济中的“中央计划委员会”,他对“普通大脑”的认知和“智慧大脑”的认知趋同的欲求,也正如中央计划委员会试图让普通大众服从于它的指令。正是市场对分散信息的分散利用使个体在固有的认知局限的情况下也有可能实现自己的目标,而大数据不能扮演同样的角色。何大安教授有关认知与大数据的讨论仍属于新古典经济学的“最大化”范式,他没意识到市场是一个互动（交换）过程,最大化范式并不适用。     

大数据时代的智慧辩护

以大数据、人工智能等为代表的智能科技正在我国刑事司法空间内主导着全面而深远的变革。相较于法检系统面对这场时代潮流所进行的主动、全面的自我革新,智能科技虽在刑事辩护领域得到了一定程度的应用,但技术与业务的交融协同有限,故总体上仍停留在初级发展阶段。实际上,刑事辩护可在大数据驱动下实现向智慧辩护的转型,包括改变律师工作方式,使之从办公自动化跨越为业务智能化;在卷宗审阅、证据审查判断、类案/关联法条推送、司法规律挖掘等工作中强化科技支撑成分,辅助律师作出辩护决策;以及重塑业务导向定位,积极开拓风险防控型业务领域。但在转型过程中,应注意在数据来源上另辟蹊径,在借鉴法院、检察院经验时因地制宜地作出调整,尤其是在法律与技术的融合中坚持住法律的主导地位,把握好智慧的根源所在。     

Ownership of personal data in the Internet of Things

This article analyses, defines, and refines the concepts of ownership and personal data to explore their compatibility in the context of EU law. It critically examines the traditional dividing line between personal and non-personal data and argues for a strict conceptual separation of personal data from personal information. The article also considers whether, and to what extent, the concept of ownership can be applied to personal data in the context of the Internet of Things (IoT). This consideration is framed around two main approaches shaping all ownership theories: a bottom-up and top-down approach. Via these dual lenses, the article reviews existing debates relating to four elements supporting introduction of ownership of personal data, namely the elements of control, protection, valuation, and allocation of personal data. It then explores the explanatory advantages and disadvantages of the two approaches in relation to each of these elements as well as to ownership of personal data in IoT at large. Lastly, this article outlines a revised approach to ownership of personal data in IoT that may serve as a blueprint for future work in this area and inform regulatory and policy debates.     

Are ‘pseudonymised’ data always personal data? Implications of the GDPR for administrative data research in the UK

There has naturally been a good deal of discussion of the forthcoming General Data Protection Regulation. One issue of interest to all data controllers, and of particular concern for researchers, is whether the GDPR expands the scope of personal data through the introduction of the term ‘pseudonymisation’ in Article 4(5). If all data which have been ‘pseudonymised’ in the conventional sense of the word (e.g. key-coded) are to be treated as personal data, this would have serious implications for research. Administrative data research, which is carried out on data routinely collected and held by public authorities, would be particularly affected as the sharing of de-identified data could constitute the unconsented disclosure of identifiable information.Instead, however, we argue that the definition of pseudonymisation in Article 4(5) GDPR will not expand the category of personal data, and that there is no intention that it should do so. The definition of pseudonymisation under the GDPR is not intended to determine whether data are personal data; indeed it is clear that all data falling within this definition are personal data. Rather, it is Recital 26 and its requirement of a ‘means reasonably likely to be used’ which remains the relevant test as to whether data are personal. This leaves open the possibility that data which have been ‘pseudonymised’ in the conventional sense of key-coding can still be rendered anonymous. There may also be circumstances in which data which have undergone pseudonymisation within one organisation could be anonymous for a third party. We explain how, with reference to the data environment factors as set out in the UK Anonymisation Network's Anonymisation Decision-Making Framework.     

A comparison of data protection legislation and policies across the EU

Although the protection of personal data is harmonized within the EU by Directive 95/46/EC and will be further harmonized by the General Data Protection Regulation (GDPR) in 2018, there are significant differences in the ways in which EU member states implemented the protection of privacy and personal data in national laws, policies, and practices. This paper presents the main findings of a research project that compares the protection of privacy and personal data in eight EU member states: France, Germany, the UK, Ireland, Romania, Italy, Sweden, and the Netherlands. The comparison focuses on five major themes: awareness and trust, government policies for personal data protection, the applicable laws and regulations, implementation of those laws and regulations, and supervision and enforcement.The comparison of privacy and data protection regimes across the EU shows some remarkable findings, revealing which countries are frontrunners and which countries are lagging behind on specific aspects. For instance, the roles of and interplay between governments, civil rights organizations, and data protections authorities vary from country to country. Furthermore, with regard to privacy and data protection there are differences in the intensity and scope of political debates, information campaigns, media attention, and public debate. New concepts like privacy impact assessments, privacy by design, data breach notifications and big data are on the agenda in some but not in all countries. Significant differences exist in (the levels of) enforcement by the different data protection authorities, due to different legal competencies, available budgets and personnel, policies, and cultural factors.