The GDPR beyond Privacy: Data-Driven Challenges for Social Scientists, Legislators and Policy-Makers

AbstractCOVID-19 vaccination is being conducted in over 200 countries and regions to control SARS-CoV-2 transmission and return to a pre-pandemic lifestyle. However, understanding when non-pharmaceutical interventions (NPIs) can be lifted as immunity builds up remains a key question for policy makers. To address this, we built a data-driven model of SARS-CoV-2 transmission for China. We estimated that, to prevent the escalation of local outbreaks to widespread epidemics, stringent NPIs need to remain in place at least one year after the start of vaccination. Should NPIs alone be capable of keeping the reproduction number (Rt) around 1.3, the synergetic effect of NPIs and vaccination could reduce the COVID-19 burden by up to 99% and bring Rt below the epidemic threshold in about 9 months. Maintaining strict NPIs throughout 2021 is of paramount importance to reduce COVID-19 burden while vaccines are distributed to the population, especially in large populations with little natural immunity.

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Using Event History Analysis in Historical Research: With Illustrations from a Study of the Passage of Women's Protective Legislation

International Review of Social History ◽

10.1017/s0020859000115081 ◽

1998 ◽

Vol 43 (S6) ◽

pp. 33-55 ◽

Cited By ~ 7

Author(s):

Holly J. McCammon

Keyword(s):

Event History Analysis ◽

Historical Research ◽

Event History ◽

Industrial Workers ◽

Social Scientists ◽

Policy Makers ◽

History Analysis

Historians and social scientists often investigate the conditions that influence the occurrence of particular events. For instance, a researcher might be concerned with the causes of revolutionary action in some countries or the forces that unleash racial rioting in major cities. Or perhaps the researcher wishes to examine why industrial workers decide to strike or what prompts policy-makers to pass new legislation. In each of these examples, a qualitative shift occurs, from a circumstance without racial rioting in a particular city, for instance, to one with racial rioting. Event history analysis can aid researchers in uncovering the conditions that lead to such a shift.

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An open toolkit for tracking open science partnership implementation and impact

Gates Open Research ◽

10.12688/gatesopenres.12958.1 ◽

2019 ◽

Vol 3 ◽

pp. 1442 ◽

Cited By ~ 2

Author(s):

E. Richard Gold ◽

Sarah E. Ali-Khan ◽

Liz Allen ◽

Lluis Ballell ◽

Manoel Barral-Netto ◽

...

Keyword(s):

Annual Report ◽

Open Data ◽

Structured Interview ◽

Open Science ◽

Social Scientists ◽

Policy Makers ◽

Ethical Concerns ◽

Research And Innovation ◽

Survey Form ◽

The Impact

Serious concerns about the way research is organized collectively are increasingly being raised. They include the escalating costs of research and lower research productivity, low public trust in researchers to report the truth, lack of diversity, poor community engagement, ethical concerns over research practices, and irreproducibility. Open science (OS) collaborations comprise of a set of practices including open access publication, open data sharing and the absence of restrictive intellectual property rights with which institutions, firms, governments and communities are experimenting in order to overcome these concerns. We gathered two groups of international representatives from a large variety of stakeholders to construct a toolkit to guide and facilitate data collection about OS and non-OS collaborations. Ultimately, the toolkit will be used to assess and study the impact of OS collaborations on research and innovation. The toolkit contains the following four elements: 1) an annual report form of quantitative data to be completed by OS partnership administrators; 2) a series of semi-structured interview guides of stakeholders; 3) a survey form of participants in OS collaborations; and 4) a set of other quantitative measures best collected by other organizations, such as research foundations and governmental or intergovernmental agencies. We opened our toolkit to community comment and input. We present the resulting toolkit for use by government and philanthropic grantors, institutions, researchers and community organizations with the aim of measuring the implementation and impact of OS partnership across these organizations. We invite these and other stakeholders to not only measure, but to share the resulting data so that social scientists and policy makers can analyse the data across projects.

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Before and beyond trust: reliance in medical AI

Journal of Medical Ethics ◽

10.1136/medethics-2020-107095 ◽

2021 ◽

pp. medethics-2020-107095

Author(s):

Charalampia (Xaroula) Kerasidou ◽

Angeliki Kerasidou ◽

Monika Buscher ◽

Stephen Wilkinson

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

New Technology ◽

Public Trust ◽

Learning Technologies ◽

Data Driven ◽

Policy Makers ◽

The Public ◽

Regulatory Frameworks ◽

Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

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NeMoR: a New Method Based on Data-Driven for Neonatal Mortality Rate Forecasting

10.1101/2021.04.22.21255916 ◽

2021 ◽

Author(s):

Carlos Eduardo Beluzo ◽

Luciana Correia Alves ◽

Natália Martins Arruda ◽

Cátia Sepetauskas ◽

Everton Silva ◽

...

Keyword(s):

Public Health ◽

Machine Learning ◽

Neonatal Mortality ◽

Regression Models ◽

Mortality Rates ◽

Data Driven ◽

Health Policies ◽

Neonatal Mortality Rate ◽

Policy Makers ◽

Public Health Policies

ABSTRACTReduction in child mortality is one of the United Nations Sustainable Development Goals for 2030. In Brazil, despite recent reduction in child mortality in the last decades, the neonatal mortality is a persistent problem and it is associated with the quality of prenatal, childbirth care and social-environmental factors. In a proper health system, the effect of some of these factors could be minimized by the appropriate number of newborn intensive care units, number of health care units, number of neonatal incubators and even by the correct level of instruction of mothers, which can lead to a proper care along the prenatal period. With the intent of providing knowledge resources for planning public health policies focused on neonatal mortality reduction, we propose a new data-driven machine leaning method for Neonatal Mortality Rate forecasting called NeMoR, which predicts neonatal mortality rates for 4 months ahead, using NeoDeathForecast, a monthly base time series dataset composed by these factors and by neonatal mortality rates history (2006-2016), having 57,816 samples, for all 438 Brazilian administrative health regions. In order to build the model, Extra-Tree, XGBoost Regressor, Gradient Boosting Regressor and Lasso machine learning regression models were evaluated and a hyperparameters search was also performed as a fine tune step. The method has been validated using São Paulo city data, mainly because of data quality. On the better configuration the method predicted the neonatal mortality rates with a Mean Square Error lower than 0.18. Besides that, the forecast results may be useful as it provides a way for policy makers to anticipate trends on neonatal mortality rates curves, an important resource for planning public health policies.Graphical AbstractHighlightsProposition of a new data-driven approach for neonatal mortality rate forecast, which provides a way for policy-makers to anticipate trends on neonatal mortality rates curves, making a better planning of health policies focused on NMR reduction possible;a method for NMR forecasting with a MSE lower than 0.18;an extensive evaluation of different Machine Learning (ML) regression models, as well as hyperparameters search, which accounts for the last stage in NeMoR;a new time series database for NMR prediction problems;a new features projection space for NMR forecasting problems, which considerably reduces errors in NRM prediction.

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PROMOTING STATISTICS IN THE ERA OF DATA SCIENCE AND DATA-DRIVEN INNOVATIONS

STATISTICS EDUCATION RESEARCH JOURNAL ◽

10.52041/serj.v19i1.132 ◽

2020 ◽

Vol 19 (1) ◽

pp. 226-237

Author(s):

IBRAHIM SIDI ZAKARI

Keyword(s):

Communication Skills ◽

Data Science ◽

Statistics Education ◽

Effective Interaction ◽

Oral Communication ◽

Public Institutions ◽

Future Generations ◽

Data Driven ◽

Policy Makers ◽

Research Journal

This paper aims at highlighting initiatives in developing future statisticians directed at high-school and university levels in Niger. More specifically, it focuses on collaborations, partnerships, outreach initiatives and supporting mechanisms, which may contribute to increase engagement and interest in and attraction to the field of statistics in the era of data science and data-driven innovations. Providing sufficient exposure to modern statistical analysis, computational and graphical tools, written and oral communication skills, and the ever-growing interdisciplinary use of statistics are key activities for building future generations of statisticians. Furthermore, current curricula as well as pedagogical approaches, teaching materials, and assessment methods need to be re-thought in order tomeet the requirements of the skills needed in the 21st century ensuring effective interaction with scientists, public institutions, industry, civil society, and policy makers. First published February 2020 at Statistics Education Research Journal Archives

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‘We Were All Very Sick and Very Stupid’

Michael Young, Social Science, and the British Left, 1945-1970 ◽

10.1093/oso/9780198862895.003.0002 ◽

2020 ◽

pp. 20-47

Author(s):

Lise Butler

Keyword(s):

Social Science ◽

Labour Party ◽

Political Organization ◽

The Political ◽

Left Wing ◽

Social Scientists ◽

Policy Makers ◽

John Bowlby ◽

Child Psychologist ◽

Social Importance

This chapter discusses the Conference on the Psychological and Sociological Problems of Modern Socialism held at University College Oxford in 1945. This event featured prominent left-wing policy makers, intellectuals, and social scientists, including the MP Evan Durbin, the political theorist G. D. H. Cole, the writer and politician Margaret Cole, the child psychologist John Bowlby, the historian R. H. Tawney, and Michael Young, who was then the Secretary of the Labour Party Research Department. The conference reflected multiple strands of inter-war and mid-twentieth century political thought and social science which emphasized the political and social importance of small groups, notably through guild socialist arguments for pluralistic forms of political organization, and theories about human attachment drawn from child psychology. The views expressed at the conference reflected a sense that active and participatory democracy was not just morally right but psychologically necessary to prevent popular political radicalization, limit the appeal of totalitarianism, and promote peaceful civil society. The chapter concludes by noting that the events of the conference, and the intellectual influences that it represented, would subsequently shape Michael Young’s project to promote social science within the Labour Party during the later years of the Attlee government.

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Introduction: Resilience and sustainability

Environment and Development Economics ◽

10.1017/s1355770x98210126 ◽

1998 ◽

Vol 3 (2) ◽

pp. 221-262 ◽

Cited By ~ 18

Author(s):

Charles Perrings

Keyword(s):

Environmental Degradation ◽

Low Income ◽

Low Income Countries ◽

Poverty Traps ◽

Economic Systems ◽

Social Scientists ◽

Policy Makers ◽

Ecological Concept ◽

Ecological Economic ◽

Lock In

One of the most interesting and potentially useful outcomes of recent collaboration between natural and social scientists concerned with the sustainability of jointly determined ecological-economic systems is the application of the ecological concept of resilience. In its broadest sense, resilience is a measure of the ability of a system to withstand stresses and shocks – its ability to persist in an uncertain world. For many policy-makers, however, the concern that desirable states or processes may not be ‘sustainable’ is balanced by the concern that individuals and societies may get ‘locked-in’ to undesirable states or processes. Many low-income countries, for example, are thought to have been caught in poverty traps, and poverty traps have since been seen as a major cause of environmental degradation (Dasgupta, 1993). Other examples of ‘lock-in’ include our dependence on hydrocarbon-based technologies, or the institutional and cultural rigidities that stand in the way of change (Hanna, Folke, and Mäler, 1996). Such states or processes are too persistent.

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Hard Men, Shop Boys and Others: Embodying Competence in a Masculinist Occupation

The Sociological Review ◽

10.1111/1467-954x.00386 ◽

2002 ◽

Vol 50 (3) ◽

pp. 334-355 ◽

Cited By ~ 56

Author(s):

Lee F. Monaghan

Keyword(s):

Risk Management ◽

Empirical Data ◽

Private Security ◽

The Body ◽

Night Time ◽

Social Scientists ◽

Policy Makers ◽

Body Build ◽

Flexible Boundaries

Emerging studies on private security work in Britain's night-time economy explore important sociological themes such as masculinities and violence. Contributing rich ethnography to this literature, and in furthering an embodied sociology, this paper describes the gendered construction of competency among ‘bouncers’ or door supervisors within the context of their potentially violent work. Centrally, it explores the door supervisors' variable bodily capital (comprising body build and acquired techniques of the body) alongside normative limits to their violence. Here physicality is central to the practicalities of doorwork, risk management and the embodiment of dominant and subordinate masculinities. Within doorwork culture, embodied typifications such as ‘hard men’, ‘shop boys’ and others (eg, ‘bullies’ and ‘nutters’) are related to assessments of possible violence against doorstaff, the delineation of (flexible) boundaries for their own (in)appropriate violence against ‘problematic’ customers and the construction of competent identity. Besides contributing empirical data to the literature this paper underscores the integrative potential of embodiment for social scientists and urges policy makers to appreciate the degree to which (potential) violence is embodied in the night-time economy.

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Predicting neighborhoods’ socioeconomic attributes using restaurant data

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1903064116 ◽

2019 ◽

Vol 116 (31) ◽

pp. 15447-15452 ◽

Cited By ~ 15

Author(s):

Lei Dong ◽

Carlo Ratti ◽

Siqi Zheng

Keyword(s):

Urban Neighborhoods ◽

Social Scientists ◽

Population Number ◽

Policy Makers ◽

Test Dataset ◽

Training Samples ◽

Socioeconomic Data ◽

Spatial Locations ◽

Enterprise Activity ◽

Number Of Firms

Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute—restaurant—can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends.

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