Partnering to Curate and Archive Social Science Data

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.

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International Social Science Council (ISSC) Standing Committee on Social Science Data Archives

Social Science Information ◽

10.1177/053901846900800208 ◽

1969 ◽

Vol 8 (2) ◽

pp. 141-145

Keyword(s):

Social Science ◽

Science Data ◽

Standing Committee ◽

Data Archives ◽

Social Science Data ◽

International Social

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2. CESSDA – a History of Research Data Management for Social Science Data

Research Data Management - A European Perspective ◽

10.1515/9783110365634-003 ◽

2017 ◽

pp. 25-42 ◽

Cited By ~ 1

Keyword(s):

Social Science ◽

Data Management ◽

Research Data ◽

Science Data ◽

Research Data Management ◽

History Of Research ◽

History Of ◽

Social Science Data

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

African and Asian Studies ◽

10.1163/15692108-12341457 ◽

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.

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