scholarly journals When 4 ≈ 10,000: The Power of Social Science Knowledge in Predictive Performance

2019 ◽  
Vol 5 ◽  
pp. 237802311881177 ◽  
Author(s):  
Stephen McKay
Keyword(s):  
Social Science ◽  
Data Science ◽  
Predictive Performance ◽  
Science Methods ◽  
Learning Approaches ◽  
Social Scientists ◽  
Individual Variables ◽  
Predicted Values ◽  
Social Scientific ◽  
New Variables

Computer science has devised leading methods for predicting variables; can social science compete? The author sets out a social scientific approach to the Fragile Families Challenge. Key insights included new variables constructed according to theory (e.g., a measure of shame relating to hardship), lagged values of the target variables, using predicted values of certain outcomes to inform others, and validated scales rather than individual variables. The models were competitive: a four-variable logistic regression model was placed second for predicting layoffs, narrowly beaten by a model using all the available variables (>10,000) and an ensemble of algorithms. Similarly, a relatively small random forest model (25 variables) was ranked seventh in predicting material hardship. However, a similar approach overfitted the prediction of grit. Machine learning approaches proved superior to linear regression for modeling the continuous outcomes. Overall, social scientists can contribute to predictive performance while benefiting from learning more about data science methods.

Why they shared: recovering early arguments for sharing social scientific data

2020 ◽  
Vol 33 (2) ◽  
pp. 101-119
Author(s):  
Emily Hauptmann
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Financial Stability ◽  
Social Research ◽  
Scientific Data ◽  
Social Scientists ◽  
Ethical Imperative ◽  
Social Scientific ◽  
The University ◽  
University Consortium

ArgumentMost social scientists today think of data sharing as an ethical imperative essential to making social science more transparent, verifiable, and replicable. But what moved the architects of some of the U.S.’s first university-based social scientific research institutions, the University of Michigan’s Institute for Social Research (ISR), and its spin-off, the Inter-university Consortium for Political and Social Research (ICPSR), to share their data? Relying primarily on archived records, unpublished personal papers, and oral histories, I show that Angus Campbell, Warren Miller, Philip Converse, and others understood sharing data not as an ethical imperative intrinsic to social science but as a useful means to the diverse ends of financial stability, scholarly and institutional autonomy, and epistemological reproduction. I conclude that data sharing must be evaluated not only on the basis of the scientific ideals its supporters affirm, but also on the professional objectives it serves.

Considering Finite Provinces of Meaning

2020 ◽  
Vol 12 ◽  
pp. 155-170
Author(s):  
Jerry Williams ◽  
Keyword(s):  
Social Science ◽  
Common Sense ◽  
Scientific Concepts ◽  
Social Scientists ◽  
Finite Provinces Of Meaning ◽  
Social Scientific

This essay considers social science as a finite province of meaning. It is argued that teasing out common-sense meanings from social scientific conceptions is difficult because the meanings of scientific concepts are often veiled in life-worldly taken-for-grantedness. If social scientists have successfully created a scientific province of meaning, attempts to communicate findings outside of this reduced sphere of science should be somewhat problematic.

‘Who Wants to be a Social Engineer? A commentary on David Blunkett's Speech to the ESRC’

2000 ◽  
Vol 5 (1) ◽  
pp. 74-84 ◽  
Author(s):  
Peter Hodgkinson
Keyword(s):  
Social Science ◽  
Policy Process ◽  
Social Research ◽  
Social Scientists ◽  
Politics And Government ◽  
The Government ◽  
Education And Employment ◽  
Social Scientific ◽  
The Uk

This article is a response to a speech addressed to the Economic and Social Research Council which was made, in February this year, by the UK Secretary of State for Education and Employment, David Blunkett. The speech was entitled ‘Influence or Irrelevance: can social science improve government?’ . Blunkett's programme for engaging social science in the policy process is far from unique and many of the arguments have been heard before. However, the curiosity of the speech lies in the fact that the conception of social science which Blunkett advocates mirrors the approach New Labour itself has to politics and government. This raises some rather interesting difficulties for social scientists. How do we engage in a debate about the role of social scientific research in the policy process when our own conception of the discipline may be radically at odds with that of the government? Furthermore, New Labour's particular conception of the relationship between social and policy-making means that we not only have to contest their notion of what it is we do, but also challenge their conception of the policy process. We cannot ignore this engagement, even if we wanted to. The challenge is to address it and to do so, moreover, in terms which Blunkett might understand. This article is an attempt to start this process.

Conclusions, Responses to Objections, and Scholarly Recommendations

2019 ◽  
pp. 240-256
Author(s):  
Michael C. Desch
Keyword(s):  
Social Science ◽  
Real World ◽  
Vested Interest ◽  
Science Methods ◽  
Intellectual Culture ◽  
Social Scientists ◽  
Social Science Methods ◽  
Self Interest ◽  
To Come ◽  
Policy Problems

This concluding chapter evaluates the increasing tendency of many social scientists to embrace methods and models for their own sake rather than because they can help people answer substantively important questions. This inclination is in part the result of the otherwise normal and productive workings of science but is also reinforced by less positive factors such as organizational self-interest and intellectual culture. As a result of the latter, many political scientists have committed themselves to particular social science methods not so much because they believe they will illuminate real-world policy problems but because they serve a vested interest in disciplinary autonomy and dovetail with a particular image of what a “science” of politics should look like. In other words, the professionalization of social science is the root of the enduring relevance question. The chapter then offers some concrete suggestions for how to reestablish the balance between rigor and relevance in the years to come.

Symposium on Scientific Explanation in Political Science: Deduction, Explanation, and Social Scientific Inquiry

1969 ◽  
Vol 63 (4) ◽  
pp. 1233-1246
Author(s):  
John G. Gunnell
Keyword(s):  
Social Science ◽  
Philosophy Of Science ◽  
Political Science ◽  
Scientific Inquiry ◽  
Scientific Explanation ◽  
Rapid Expansion ◽  
Social Scientists ◽  
Methodological Problems ◽  
Social Scientific ◽  
The Many

The purpose here is to explore certain aspects of the philosophy of science which have serious implications both for the practice of social and political science and for understanding that practice. The current relationship between social science and the philosophy of science (or the philosophy of the social sciences) is a curious one. Despite the emergence of a considerable body of literature in philosophy which is pertinent to the methodological problems of social science, there has been a lack of osteusive ties between the two areas. A justified concern with the independence of social scientific research has contributed to a tendency toward isolation which is unfortunate in view of the proliferation of philosophical problems which necessarily attends the rapid expansion of any empirical discipline. Although in the literature of contemporary social science there are frequent references to certain works in the philosophy of science and to philosophical issues relating to methodology, these are most often in the context of bald pronouncements and shibboleths relating to the nature of science, its goals, and the character of its reasoning. But what is most disturbing about the fact that social scientists have little direct and thorough acquaintance with the philosophy of science is not merely that there has been a failure to carefully examine the many logical and epistemological assumptions which are implicit in social scientific inquiry, since this task might normally and properly be considered to be within the province of the philospher of science.

Feminism and social science

2018 ◽  
Author(s):  
Alison Wylie
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Theoretical Basis ◽  
Social Scientists ◽  
Feminist Activism ◽  
The Social ◽  
Social Scientific ◽  
Contextual Values ◽  
Second Wave

Feminists have two sorts of interest in the social sciences. With the advent of the second-wave women’s movement, they developed wide-ranging critiques of gender bias in the conceptual framework and methodology, as well as in the goals, institutions and practice of virtually all the social sciences; they argue that the social sciences both reflect and contribute to the sexism of the larger societies in which they are embedded. Alongside these critiques feminist practitioners have established constructive programmes of research that are intended to rectify the inadequacies of existing traditions of research and to address questions of concern to women. In this they are committed both to improving the disciplines in which they participate and to establishing a sound empirical and theoretical basis for feminist activism. This engagement of feminists with social science, as commentators and practitioners, raises a number of philosophical issues that have been addressed by feminist social scientists and philosophers. These include questions about ideals of objectivity and the role of contextual values in social scientific inquiry, the goals of feminist research, the forms of practice appropriate to these goals, and the responsibilities of feminist researchers to the subjects of inquiry and to those who may otherwise be affected by its conduct or results.

scholarly journals Why resilience is unappealing to social science: Theoretical and empirical investigations of the scientific use of resilience

2015 ◽  
Vol 1 (4) ◽  
pp. e1400217 ◽  
Author(s):  
Lennart Olsson ◽  
Anne Jerneck ◽  
Henrik Thoren ◽  
Johannes Persson ◽  
David O’Byrne
Keyword(s):  
Social Science ◽  
Resilience Theory ◽  
Social Scientists ◽  
Interdisciplinary Dialogue ◽  
Sustainability Research ◽  
The Social ◽  
Core Concepts ◽  
Social Scientific ◽  
And Function ◽  
Integrated Sustainability

Resilience is often promoted as a boundary concept to integrate the social and natural dimensions of sustainability. However, it is a troubled dialogue from which social scientists may feel detached. To explain this, we first scrutinize the meanings, attributes, and uses of resilience in ecology and elsewhere to construct a typology of definitions. Second, we analyze core concepts and principles in resilience theory that cause disciplinary tensions between the social and natural sciences (system ontology, system boundary, equilibria and thresholds, feedback mechanisms, self-organization, and function). Third, we provide empirical evidence of the asymmetry in the use of resilience theory in ecology and environmental sciences compared to five relevant social science disciplines. Fourth, we contrast the unification ambition in resilience theory with methodological pluralism. Throughout, we develop the argument that incommensurability and unification constrain the interdisciplinary dialogue, whereas pluralism drawing on core social scientific concepts would better facilitate integrated sustainability research.

Social Science Data Analysis

2013 ◽  
pp. 131-147
Author(s):  
Anthony Scime ◽  
Gregg R. Murray
Keyword(s):  
Data Mining ◽  
Social Science ◽  
Large Data ◽  
Data Sets ◽  
Health And Wellness ◽  
Science Data ◽  
Social Scientists ◽  
The Social ◽  
Social Scientific ◽  
Behavioral Predictions

Social scientists address some of the most pressing issues of society such as health and wellness, government processes and citizen reactions, individual and collective knowledge, working conditions and socio-economic processes, and societal peace and violence. In an effort to understand these and many other consequential issues, social scientists invest substantial resources to collect large quantities of data, much of which are not fully explored. This chapter proffers the argument that privacy protection and responsible use are not the only ethical considerations related to data mining social data. Given (1) the substantial resources allocated and (2) the leverage these “big data” give on such weighty issues, this chapter suggests social scientists are ethically obligated to conduct comprehensive analysis of their data. Data mining techniques provide pertinent tools that are valuable for identifying attributes in large data sets that may be useful for addressing important issues in the social sciences. By using these comprehensive analytical processes, a researcher may discover a set of attributes that is useful for making behavioral predictions, validating social science theories, and creating rules for understanding behavior in social domains. Taken together, these attributes and values often present previously unknown knowledge that may have important applied and theoretical consequences for a domain, social scientific or otherwise. This chapter concludes with examples of important social problems studied using various data mining methodologies including ethical concerns.

Debating the Reality of Race, Caste, and Ethnicity

2017 ◽  
Vol 48 (2) ◽  
pp. 139-167 ◽  
Author(s):  
Harold Kincaid
Keyword(s):  
Social Science ◽  
Scientific Realism ◽  
Sufficient Conditions ◽  
Necessary And Sufficient Conditions ◽  
Social Scientists ◽  
Ongoing Debate ◽  
Science Explanation ◽  
Social Scientific ◽  
Necessary And Sufficient ◽  
Realism Question

There is a lively ongoing debate among philosophers and social scientists about the reality of race and among social scientists about the reality of caste and ethnicity. This paper tries to sort out what the issues are and makes some preliminary suggestions about what the evidence shows. Standard philosophical analyses try to find the necessary and sufficient conditions of our concept of race. I argue that this is not the best way to approach the issue and that the reality of these concepts should be taken as a scientific realism question; that is, do our best social scientific accounts of these phenomena show that appealing to the concepts of race, caste, and ethnicity is essential to successful social science explanation? I argue that in some cases that is the case and lay out the empirical issues involved.

scholarly journals Explanation, prediction, and causality: Three sides of the same coin?

2018 ◽  
Author(s):  
Duncan J Watts ◽  
Emorie D Beck ◽  
Elisa Jayne Bienenstock ◽  
Jake Bowers ◽  
Aaron Frank ◽  
...  
Keyword(s):  
Social Science ◽  
Predictive Accuracy ◽  
Social Systems ◽  
Predictive Performance ◽  
Computational Social Science ◽  
Machine Learning Algorithms ◽  
Social Scientists ◽  
Common Task ◽  
The Common ◽  
Do So

In this essay we make four interrelated points. First, we reiterate previous arguments (Kleinberg et al 2015) that forecasting problems are more common in social science than is often appreciated. From this observation it follows that social scientists should care about predictive accuracy in addition to unbiased or consistent estimation of causal relationships. Second, we argue that social scientists should be interested in prediction even if they have no interest in forecasting per se. Whether they do so explicitly or not, that is, causal claims necessarily make predictions; thus it is both fair and arguably useful to hold them accountable for the accuracy of the predictions they make. Third, we argue that prediction, used in either of the above two senses, is a useful metric for quantifying progress. Important differences between social science explanations and machine learning algorithms notwithstanding, social scientists can still learn from approaches like the Common Task Framework (CTF) which have successfully driven progress in certain fields of AI over the past 30 years (Donoho, 2015). Finally, we anticipate that as the predictive performance of forecasting models and explanations alike receives more attention, it will become clear that it is subject to some upper limit which lies well below deterministic accuracy for many applications of interest (Martin et al 2016). Characterizing the properties of complex social systems that lead to higher or lower predictive limits therefore poses an interesting challenge for computational social science.

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