The Provenance Problem: Research Methods and Ethics in the Age of WikiLeaks

Author(s):  
CHRISTOPHER DARNTON

How should political scientists navigate the ethical and methodological quandaries associated with analyzing leaked classified documents and other nonconsensually acquired sources? Massive unauthorized disclosures may excite qualitative scholars with policy revelations and quantitative researchers with big-data suitability, but they are fraught with dilemmas that the discipline has yet to resolve. This paper critiques underspecified research designs and opaque references in the proliferation of scholarship with leaked materials, as well as incomplete and inconsistent guidance from leading journals. It identifies provenance as the primary concept for improved standards and reviews other disciplines’ approaches to this problem. It elaborates eight normative and evidentiary criteria for scholars by which to assess source legitimacy and four recommendations for balancing their trade-offs. Fundamentally, it contends that scholars need deeper reflection on source provenance and its consequences, more humility about whether to access new materials and what inferences to draw, and more transparency in citation and research strategies.

1981 ◽  
Vol 6 (3) ◽  
pp. 164-174 ◽  
Author(s):  
Mark A. Koorland ◽  
David L. Westling

Assistance for the consumer in understanding Applied Behavior Analysis (ABA) research strategies and technical aspects is provided. Various measures of behavior and reliability are discussed. Experimental strategies and variations of reversal and multiple baseline research designs are reviewed with particular attention given to experimental logic. It is felt if the consumer understands the principles guiding researchers, then accurate appraisal and useful applications of ABA research outcomes are likely.


Author(s):  
Haixuan Zhu ◽  
◽  
Xiaoyu Jia ◽  
Pengluo Que ◽  
Xiaoyu Hou ◽  
...  

In the era of big data, with the development of computer technology, especially the comprehensive popularization of mobile terminal device and the gradual construction of the Internet of Things, the urban physical environment and social environment have been comprehensively digitized and quantified. Computational thinking mode has gradually become a new thinking mode for human beings to recognize and govern urban complex system. Meanwhile computational urban science has become the main discipline development aspect of modern urban planning. Computational thinking is the thinking of computer science using algorithms based on time complexity and space complexity, which provides a new paradigm for the construction of index system, data collection, data storage, data analysis, pattern recognition, dynamic governance in the process of scientific planning and urban management. Based on this, this paper takes the computational thinking mode of urban planning discipline in big data era as the research object, takes the scientific construction of computational urban planning as the research purpose, and adopts literature research methods and interdisciplinary research methods, comprehensively studies the connotation of the computing thinking mode of computer science. Meanwhile, this paper systematically discusses the system construction of urban computing, model generation, the theory and method of digital twinning, as well as the popularization of the computational thinking mode of urban and rural planning discipline and the scientific research of computational urban planning, which responds to the needs of the era of the development of urban and rural planning disciplines in the era of big data.


2017 ◽  
Vol 8 (1) ◽  
pp. 51-72
Author(s):  
Jin-seo Park

Qualitative research methods based on literature review or expert judgement have been used to find core issues, analyze emerging trends and discover promising areas for the future. Deriving results from large amounts of information under this approach is both costly and time consuming. Besides, there is a risk that the results may be influenced by the subjective opinion of experts. In order to make up for such weaknesses, the analysis paradigm for choosing future emerging trend is undergoing a shift toward mplementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The hange used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and the promising areas for the future from research papers pertaining to overall aviation areas through text mining method, which is one of the big data analysis techniques. This study has limitations in that its analysis for retrieving the aviation-related core issues and promising fields was restricted to research papers containing the keyword "aviation." However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and emerging trends regarding the promising areas for the future in the aviation industry through the application of a big data-based descriptive approach.


2019 ◽  
Vol 8 (2S11) ◽  
pp. 3606-3611

Big data privacy has assumed importance as the cloud computing became a phenomenal success in providing a remote platform for sharing computing resources without geographical and time restrictions. However, the privacy concerns on the big data being outsourced to public cloud storage are still exist. Different anonymity or sanitization techniques came into existence for protecting big data from privacy attacks. In our prior works, we have proposed a misusability probability based metric to know the probable percentage of misusability. We additionally planned a system that suggests level of sanitization before actually applying privacy protection to big data. It was based on misusability probability. In this paper, our focus is on further evaluation of our misuse probability based sanitization of big data approach by defining an algorithm which willanalyse the trade-offs between misuse probability and level of sanitization. It throws light into the proposed framework and misusability measure besides evaluation of the framework with an empirical study. Empirical study is made in public cloud environment with Amazon EC2 (compute engine), S3 (storage service) and EMR (MapReduce framework). The experimental results revealed the dynamics of the trade-offs between them. The insights help in making well informed decisions while sanitizing big data to ensure that it is protected without losing utility required.


Author(s):  
Andrew N. Pilny ◽  
Marshall Scott Poole

The exponential growth of “Big Data” has given rise to a field known as computational social science (CSS). The authors view CSS as the interdisciplinary investigation of society that takes advantage of the massive amount of data generated by individuals in a way that allows for abductive research designs. Moreover, CSS complicates the relationship between data and theory by opening the door for a more data-driven approach to social science. This chapter will demonstrate the utility of a CSS approach using examples from dynamic interaction modeling, machine learning, and network analysis to investigate organizational communication (OC). The chapter concludes by suggesting that lessons learned from OC's history can help deal with addressing several current issues related to CSS, including an audit culture, data collection ethics, transparency, and Big Data hubris.


Author(s):  
Kevin R. Murphy

Performance management developed out of, and in part in reaction to, traditional performance appraisal systems. Despite frequent claims in the business press that performance appraisal is dying, fairly traditional appraisal systems are still common in work organizations. However, there is evidence of an ongoing shift toward performance management systems that differ from traditional performance appraisal systems in several important ways. The types of performance management systems exemplified in the case studies included in this volume place more emphasis on frequent, informal evaluation; real-time feedback; and alignment with organizational strategies and goals than is common in traditional appraisal systems. These types of performance management systems have a lot to offer, and the case studies illustrate the potential advantage of modern approaches to performance management. Unfortunately, these case studies also exemplify a deeply worrying trend in performance management: a frequent failure to even ask whether (much less to demonstrate that) performance management has any real effect on performance or effectiveness. Some recommendations are made regarding research strategies for evaluating performance management; several of the most pressing challenges in this endeavor are identified, notably the problematic status of feedback and the trade-offs involved when moving from formal appraisal systems to systems built around informal feedback.


Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.


Author(s):  
Judith Mavodza

The library and information science (LIS) profession is influenced by multidisciplinary research strategies and techniques (research methods) that in themselves are also evolving. They represent established ways of approaching research questions (e.g., qualitative vs. quantitative methods). This chapter reviews the methods of research as expressed in literature, demonstrating how, where, and if they are inter-connected. Chu concludes that popularly used approaches include the theoretical approach, experiment, content analysis, bibliometrics, questionnaire, and interview. It appears that most empirical research articles in Chu's analysis employed a quantitative approach. Although the survey emerged as the most frequently used research strategy, there is evidence that the number and variety of research methods and methodologies have been increasing. There is also evidence that qualitative approaches are gaining increasing importance and have a role to play in LIS, while mixed methods have not yet gained enough recognition in LIS research.


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