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 Relying primarily on archived records, unpublished personal papers, and oral histories, I show that Angus Campbell, Warren Miller, Philip Converse, and others understood sharing data not as an ethical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.

‘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.

The Edwards' Report and the International Relations profession

1988 ◽  
Vol 14 (2) ◽  
pp. 149-152 ◽  
Author(s):  
Michael Nicholson
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
International Relations ◽  
Research Council ◽  
Social Research ◽  
Future Research ◽  
Social Scientists ◽  
Social Science Community ◽  
The Social ◽  
Science Community

The Economic and Social Research Council recently published a Report commissioned from a committee chaired by Professor Edwards, a psychiatrist, so that the Council, and the social science community in general, might know what was good and bad in British social sciences, and where the promising future research opportunities lie over the next decade. Boldly called ‘Horizons and Opportunities in the Social Sciences’, the Report condensed the wisdom of social scientists, both British and foreign, and concludes with a broadly but not uncritically favourable picture of the British scene.

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.

University of East Africa Social Science Conference

1966 ◽  
Vol 4 (4) ◽  
pp. 538-540
Author(s):  
Martin Lowenkopf
Keyword(s):  
Social Science ◽  
Political Science ◽  
East Africa ◽  
Sierra Leone ◽  
Rural Settlement ◽  
Social Scientists ◽  
Science Conference ◽  
The University ◽  
Political Decisions ◽  
Economic Criteria

This conference brought together over 70 social scientists from the Kenyan, Tanzanian, and Ugandan constituent Colleges of the University of East Africa (with visitors from Zambia, Ethiopia, Sierra Leone, and Rhodesia) for their annual inter-disciplinary, or rather trans-disciplinary, deliberations. Why ‘trans-disciplinary’? Because the historians discussed nationalism, politics, and church movements; political scientists discoursed on economics, rural settlement, agriculture, and education; sociologists criticised political decisions and economic criteria which hampered their investigations into resettlement programmes; and the economists, while speaking mostly about economics, were represented at virtually all panels, apparently to guard their disciplinary preserve against intrusions, presumptions and, in one case, elision with political science.

Imperatives for Theories in Social Science: The North-South Divide Notwithstanding

2020 ◽  
Vol 19 (3) ◽  
pp. 195-217
Author(s):  
Aaron Ola Ogundiwin, ◽  
Joel N. Nwachukwu
Keyword(s):  
Social Science ◽  
Conceptual Analysis ◽  
Social Research ◽  
Science Data ◽  
Social Scientists ◽  
The North ◽  
Common Mistake ◽  
History Of ◽  
Research Questions ◽  
Social Science Data

Abstract The paper underscores the place of theories in organizing social science data and experience. It holds that theories are indispensable to social research (The North-South divide notwithstanding), in view of the fact that the framework of knowledge and experience within which theories are established make a meaningful explanation of the world phenomenon reasonably possible. It delineates political philosophy and history of ideas from theory and thus, takes care of common mistake social scientists make differentiating between them. Furthermore, the paper on one hand, takes on the scientific requisites of theory such as assumption, concepts (and their functions), hypothesis (and its characteristics typology), law, models, paradigm and provides lucid conceptual analysis of each with a view to showing their relatedness to theory but not as synonyms to it. On the other hand, we singled out dependency theory in its emanation from knowledge and experience of underdevelopment of Third World countries, as the first and perhaps most relevant theoretic explanation of Africa’s underdevelopment. The paper posits that a good theory that will serve as a rudder for formulation of research questions, problem statement, as well as sustain the data analysis, and findings must parade some, if not all of the following qualities: precision and testability, empirical validity, parsimony, stimulation, and practicability.

Big Data Sharing Among Academics

2013 ◽  
pp. 177-194 ◽  
Author(s):  
Jeonghyun Kim
Keyword(s):  
Big Data ◽  
Social Science ◽  
Case Studies ◽  
Data Sharing ◽  
Physical Science ◽  
Life Science ◽  
Social Research ◽  
Data Repositories ◽  
Current Technology ◽  
Oceanographic Data

The goal of this chapter is to explore the practice of big data sharing among academics and issues related to this sharing. The first part of the chapter reviews literature on big data sharing practices using current technology. The second part presents case studies on disciplinary data repositories in terms of their requirements and policies. It describes and compares such requirements and policies at disciplinary repositories in three areas: Dryad for life science, Interuniversity Consortium for Political and Social Research (ICPSR) for social science, and the National Oceanographic Data Center (NODC) for physical science.

Social Sciences in Contemporary Turkey

1971 ◽  
Vol 5 (2) ◽  
pp. 72-82
Author(s):  
Walter F. Weiker
Keyword(s):  
Social Sciences ◽  
Developing Countries ◽  
Social Science ◽  
Social Research ◽  
State Of The Art ◽  
The State ◽  
Previous Article ◽  
Social Scientists ◽  
Time Space ◽  
Growing Body

In a previous article I sought to appraise the field of Turkish studies, for the most part among western (predominantly American) scholars (MESA Bulletin, Vol. 3, No. 3, October 15, 1969). To fill out the picture, it is appropriate to also view the state of social research among the rapidly growing body of Turkish teachers and researchers. This article is not, however, a direct parallel to others in the MESA “State of the Art” series, in that it is not basically bibliographical. Such a review would require far more time, space, and knowledge in depth of several other social science disciplines than is currently available to me, because despite the remarks made below about problems of definition, the quantity and technical sophistication of work by Turkish researchers is quite large and is growing rapidly. Furthermore, since most of the research referred to below is in Turkish, the number of persons to whom a bibliographic review might be useful is quite limited. Instead, I think it would be more interesting to MESA members and other American social scientists to examine the characteristics and problems of what is probably one of the most vigorous social science communities in the “developing” countries, with a view (among other things) to helping facilitate increased cooperation between Turkish and American scholars in our common endeavors of advancing the state of knowledge.

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.

scholarly journals The Logic of Qualitative Comparative Analysis

1998 ◽  
Vol 43 (S6) ◽  
pp. 105-124 ◽  
Author(s):  
Charles C. Ragin
Keyword(s):  
Social Science ◽  
Social Research ◽  
Qualitative Comparative Analysis ◽  
Research Strategies ◽  
Social Scientists ◽  
Social Phenomena ◽  
Science Theory ◽  
Common Strategy ◽  
The One ◽  
Interpretive Understanding

Social scientists often face a fundamental dilemma when they conduct social research. On the one hand, they may emphasize the complexity of social phenomena – a common strategy in ethnographic, historical and macro social research – and offer in–depth case studies sensitive to the specificity of the things they study. On the other hand, they may make broad, homo genizing assumptions about cases, and document generalities – patterns hold across many instances. Research strategies that focus on complexity are often labeled “qualitative”, “case–oriented”, “small–N”, or “intensive”. Those that focus on generality are often labeled “quantitative”, “variable–oriented”, “large–N”, or “extensive”. While the contrasts between these two types social research are substantial, it is easy to exaggerate their differences and t o caricature the two approaches, for example, portraying quantitative work on general patterns as scientific but sterile and oppressive, and qualitative research on small Ns as rich and emancipatory but journalistic. It is important to avoid these caricatures because the contrasts between these two general approaches provide important leads both for finding a middle path between them and for resolving basic methodological issues in social science Social scientists who study cases in an in–depth manner often see empiri cal generalizations simply as a means to another end – the interpretive understanding of cases. In this view, a fundamental goal of social science is t o interpret significant features of the social world and thereby advance our collective understanding of how existing social arrangements came about and why we live the way we do. The rough general patterns that social scientists may be able to identify simply aid the understanding of specific cases; they are not viewed as predictive. Besides, the task of interpreting and then representing socially significant phenomena (or the task of making selected social phenomena significant by representing them) is a much more immediate and tangible goal. In this view, empirical generalizations and social science theory are important – to the extent that they aid the goal interpretive understanding.

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.

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