Uncomfortable Decisions

Some decision-making processes are uncomfortable. Many of us do not like to make significant decisions, such as whether to have a child, solely based on social science research. We do not like to choose randomly, even in cases where flipping a coin is plainly the wisest choice. We are often reluctant to defer to another person, even if we believe that the other person is wiser, and have similar reservations about appealing to powerful algorithms. And, while we are comfortable with considering and weighing different options, there is something strange about deciding solely on a purely algorithmic process, even one that takes place in our own heads.What is the source of our discomfort? We do not present a decisive theory here—and, indeed, the authors have clashing views over some of these issues—but we lay out the arguments for two (consistent) explanations. The first is that such impersonal decision-making processes are felt to be a threat to our autonomy. In all of the examples above, it is not you who is making the decision, it is someone or something else. This is to be contrasted with personal decision-making, where, to put it colloquially, you “own” your decision, though of course you may be informed by social science data, recommendations of others, and so on. A second possibility is that such impersonal decision-making processes are not seen as authentic, where authentic decision making is one in which you intentionally and knowledgably choose an option in a way that is “true to yourself.” Such decision making can be particularly important in contexts where one is making a life-changing decision of great import, such as the choice to emigrate, start a family, or embark on a major career change.

Coming Out of the Closet and Its Influence, 1985–2013

Chapter 7 provides a variety of social science data analysis to show that contact between gays and lesbians and their straight family and friends was responsible for the dramatic liberalization of attitudes toward gay rights in the U.S. When Americans were asked why they became more supportive of marriage equality, they overwhelmingly explained that having a gay friend or family member helped them see the issue more positively. Having a gay friend was influential even to people who were not predisposed to support gay rights. Data show that gays and lesbians in the U.S. were coming out of the closet for the first time in the 1990s, and the 1990s is also when American attitudes toward gay rights started to liberalize.

Litigating Racial Profiling: The Use of Statistical Data

The use of statistical data to prove racial discrimination by police in individual cases is relatively novel in Australia. Based on a survey of international strategies, this article argues that statistical and social science data can play three critical evidential roles in litigation. Firstly, it can form part of the social context evidence used to influence the inferences that can be drawn from other evidence led in a case. Secondly it can influence the cogency of the evidence required for claimants to meet the standard of proof, and thirdly, it can be used to shift the burden of proof. Using these evidential methods, evidence of institutional racism can be used to assist in making findings of discrimination in individual cases. This article speculates on the role that statistics could have played in the Haile-Michael race discrimination claim that settled in 2013, and in the 2019 inquest into the death of Tanya Day.

THE EFFECT MIND MAPPING TECHNIQUE AND VOCABULARY MASTERY ON STUDENT’S WRITING SKILL

The purposes of this research is to know the effect mind mapping technique and vocabulary mastery on students’writing skill. Also how the implementation mind mapping technique on students’writing skill. Students need vocabulary mastery to arrange the sentences for getting a good paragraph and mind mapping technique is one of the technique that use to treat students for getting writing well. This research use experiments method. There were 40 students chosen at random from fourth grade semester English Education at STKIP Kusuma Negara Jakarta.Collecting data use test writing skill. Research of instrument has tested and tried by the validity and reliability test used the SPSS (Statistic Program Social Science). Data analyzed by using kolmogorv Smirov, know data was normality.The text analysis is to test the data homogeneity by using Levenu’s test, to see if the population was derived from homogeneous population. The result of this research concluded that 1) Using Mind Mapping Technique has significant effect on student’s writing skill. Result F for mind mapping (A) Fo=290.285 and sig.= 0.000<0.05, then the conclusion is that there is significant effect between mind mapping technique (X1) on student’s writing skills. 2). Vocabulary mastery gave the significant effect to the student’s writing skill. Result research F for vocabulary mastery (B) Fo=91.401 and sig.= 0.000<0.05. It has shown that there is significant effect vocabulary mastery (X2) on student’s writing skills. 3) There is significant effect interaction between mind mapping and vocabulary mastery on student’s writing skill. Result of this research F from mind mapping (A) and vocabulary (B) Fo=5.274 and sig.=0.028<0.05. Using mind mapping technique in learning give effect in students’writing skill. Students who have rich vocabulary mastery can get good writing English.Keywords: Podcasts, Promote, Speaking, Online Learning

Learning to See: Convolutional Neural Networks for the Analysis of Social Science Data

We provide an introduction of the functioning, implementation, and challenges of convolutional neural networks (CNNs) to classify visual information in social sciences. This tool can help scholars to make more efficient the tedious task of classifying images and extracting information from them. We illustrate the implementation and impact of this methodology by coding handwritten information from vote tallies. Our paper not only demonstrates the contributions of CNNs to both scholars and policy practitioners, but also presents the practical challenges and limitations of the method, providing advice on how to deal with these issues.

The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning

Principled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repurpose denoising autoencoders for multiple imputation by treating missing values as an additional portion of corrupted data and drawing imputations from a model trained to minimize the reconstruction error on the originally observed portion. Systematic tests on simulated as well as real social science data, together with an applied example involving a large-scale electoral survey, illustrate MIDAS’s accuracy and efficiency across a range of settings. We provide open-source software for implementing MIDAS.

Access Some Areas: Reforming Access Categories for Data in a Social Science Data Archive

In this paper we outline the process of revising data access categories for research data sets in GESIS – a large European social science data archive based in Germany. The challenge is to create a minimal set of workable access conditions that cope with a) facilitating as “open as possible, closed as necessary” expectations for data reuse; b) map on to existing legacy access categories and conditions in a data archive. The paper covers the work done in gathering data on data access categories used by data archives in their existing data catalogues, the choices offered to depositors of data in their user agreements, and work done by other data reuse platforms in categorising access to their data. Finally, we talk through the process of refining a minimal set of data access conditions for the GESIS data archive.