The Haitun dichotomy and the relevance of Bradford's law

1984 ◽  
Vol 8 (1) ◽  
pp. 19-24 ◽  
Author(s):  
B.C. Brookes

In a critical review of all the empirical laws of bibliometrics and scientometrics, the Russian statistician S.D. Haitun has shown that the application of modern statistical theory to social science data is 'inadmissible', i.e. it 'does not work'. Haitun thus points to the need to develop a wholly new statistical theory for the social sciences in general and for informetrics in particular. This paper discusses the implications of Haitun's work and explains why the older Bradford law still has an important role to play in the development of a new theory.

2020 ◽  
Author(s):  
Academy of Sociology

With these guidelines the Academy of Sociology (a German professional association) gives recommendations on how social science data could be made open. The aim is to make the Social Sciences more open.


2016 ◽  
Vol 29 (2) ◽  
pp. 62-73
Author(s):  
Kalpana Shankar ◽  
Kristin R. Eschenfelder ◽  
Greg Downey

We map out a new arena of analysis for knowledge and cyberinfrastructure scholars: Social Science Data Archives (SSDA). SSDA have influenced the international development of the social sciences, research methods, and data standards in the latter half of the twentieth century. They provide entry points to understand how fields organise themselves to be ‘data intensive’. Longitudinal studies of SSDA can increase our understanding of the sustainability of knowledge infrastructure more generally. We argue for special attention to the following themes: the co-shaping of data use and users, the materiality of shifting revenue sources, field level relationships as an important component of infrastructure, and the implications of centralisation and federation of institutions and resources. We briefly describe our ongoing study of primarily quantitative social science data archives. We conclude by discussing how cross-institutional and longitudinal analyses can contribute to the scholarship of knowledge infrastructure.Keywords: social sciences; data archives; institutional sustainability


2021 ◽  
pp. 1-19
Author(s):  
Michelle Torres ◽  
Francisco Cantú

Abstract 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.


2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Kevin Louis Bardosh ◽  
Daniel H. de Vries ◽  
Sharon Abramowitz ◽  
Adama Thorlie ◽  
Lianne Cremers ◽  
...  

Abstract Background The importance of integrating the social sciences in epidemic preparedness and response has become a common feature of infectious disease policy and practice debates. However to date, this integration remains inadequate, fragmented and under-funded, with limited reach and small initial investments. Based on data collected prior to the COVID-19 pandemic, in this paper we analysed the variety of knowledge, infrastructure and funding gaps that hinder the full integration of the social sciences in epidemics and present a strategic framework for addressing them. Methods Senior social scientists with expertise in public health emergencies facilitated expert deliberations, and conducted 75 key informant interviews, a consultation with 20 expert social scientists from Africa, Asia and Europe, 2 focus groups and a literature review of 128 identified high-priority peer reviewed articles. We also analysed 56 interviews from the Ebola 100 project, collected just after the West African Ebola epidemic. Analysis was conducted on gaps and recommendations. These were inductively classified according to various themes during two group prioritization exercises. The project was conducted between February and May 2019. Findings from the report were used to inform strategic prioritization of global investments in social science capacities for health emergencies. Findings Our analysis consolidated 12 knowledge and infrastructure gaps and 38 recommendations from an initial list of 600 gaps and 220 recommendations. In developing our framework, we clustered these into three areas: 1) Recommendations to improve core social science response capacities, including investments in: human resources within response agencies; the creation of social science data analysis capacities at field and global level; mechanisms for operationalizing knowledge; and a set of rapid deployment infrastructures; 2) Recommendations to strengthen applied and basic social sciences, including the need to: better define the social science agenda and core competencies; support innovative interdisciplinary science; make concerted investments in developing field ready tools and building the evidence-base; and develop codes of conduct; and 3) Recommendations for a supportive social science ecosystem, including: the essential foundational investments in institutional development; training and capacity building; awareness-raising activities with allied disciplines; and lastly, support for a community of practice. Interpretation Comprehensively integrating social science into the epidemic preparedness and response architecture demands multifaceted investments on par with allied disciplines, such as epidemiology and virology. Building core capacities and competencies should occur at multiple levels, grounded in country-led capacity building. Social science should not be a parallel system, nor should it be “siloed” into risk communication and community engagement. Rather, it should be integrated across existing systems and networks, and deploy interdisciplinary knowledge “transversally” across all preparedness and response sectors and pillars. Future work should update this framework to account for the impact of the COVID-19 pandemic on the institutional landscape.


2001 ◽  
Vol 25 (2) ◽  
pp. 24
Author(s):  
Janez Stebe ◽  
Irena Vipavc

The Social Science Data Archive in Slovenia


1995 ◽  
Vol 20 (2) ◽  
pp. 115-147 ◽  
Author(s):  
David Draper

Hierarchical models (HMs; Lindley & Smith, 1972) offer considerable promise to increase the level of realism in social science modeling, but the scope of what can be validly concluded with them is limited, and recent technical advances in allied fields may not yet have been put to best use in implementing them. In this article, I (a) examine 3 levels of inferential strength supported by typical social science data-gathering methods, and call for a greater degree of explicitness, when HMs and other models are applied, in identifying which level is appropriate; (b) reconsider the use of HMs in school effectiveness studies and meta-analysis from the perspective of causal inference; and (c) recommend the increased use of Gibbs sampling and other Markov-chain Monte Carlo (MCMC) methods in the application of HMs in the social sciences, so that comparisons between MCMC and better-established fitting methods—including full or restricted maximum likelihood estimation based on the EM algorithm, Fisher scoring, and iterative generalized least squares—may be more fully informed by empirical practice.


1933 ◽  
Vol 26 (4) ◽  
pp. 210-221
Author(s):  
W. L. Crum

Statistics and statistical methods have become so common a tool of the scientist, both for conducting his research and reporting his findings, that the ordinary layman is in danger of forgetting that statistical theory is a branch of mathematical science and that statistical practice is one type of applied mathematics. This important point should be constantly in the mind of everyone who uses statistics, whether as a producer or as a consumer. Nowhere is the danger of ignoring or forgetting this injunction greater than in the various fields of social science. Whether this is due to the great abundance of factual data and their ostensible bearing upon problems of timely interest, to the supposed necessity of putting reasoning in these fields on a quantitative basis at any cost, to the absence of traditional dominance by mathematical thought processes in these fields, or to other reasons, it is idle to speculate. The essential fact is that statistical workers in social science need particularly to be reminded that the vigorous and growing structure of statistical theory takes its root in mathematics.


1978 ◽  
Vol 2 (1) ◽  
pp. 3
Author(s):  
Alice Robbin

The Impact of Computer Networking on the Social Science Data Library


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