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This special section of the Journal of Empirical Research on Human Research Ethics ( JERHRE) is based upon the assumption that much of the best empirical data relevant to research ethics is hidden from the view of Research Ethics Committee (REC) members and others who are interested in research ethics. There are at least three different senses in which ethics-relevant empirical research may be hidden: (1) it may be published in a journal that ethics committee members would not regularly read, (2) it may not use key words that would guide one to its ethics-relevant content, or (3) it may be sequestered in part of a research article that is about something else. This special section of JERHRE reviews all of these types of "hidden ethics" articles on the following issues: What is the relative frequency of hidden ethics articles in journals that focus on vulnerable populations? What does the non-ethics literature in clinical research and experimental economic decision theory teach us about ways of improving subjects' comprehension of risk information? How satisfied are parents and children with their experience with pediatric psychotrophic medication trials? And, how can retention rates be improved in longitudinal studies of difficult regimens such as drug rehabilitation? There is a major amount of ethics-relevant literature that is hidden. Without better ways of communicating the existence of this literature through use of key words, or recasting of the information to highlight its relevance to research ethics in journals that ethics committee members read, the benefits of evidence-based ethical problem solving will be lost.

Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.

Insun Kim and Kenneth G. Keppel. ; Caption title. ; Shipping list no.: 98-0100-P. ; "December 1997." ; Also available via Internet from the NCHS web site (PDF file only). ; Includes bibliographical references (p. 10).

The paper presents a brief history of the development of operations management (OM). This provides the backdrop for a content analysis of journal articles published in the Journal of Operations Management and the International Journal of Operations & Production Management between January 1990 and June 2003. MBA student survey data are then used to explore any gaps that may exist between the focus of academic research and the perceived importance of given OM subject areas to practitioners. The practical and conceptual insights highlighted are then used as the basis for a discussion of extant research priorities. The paper concludes with a preliminary conceptual framework that distinguishes between OM research seeking to consolidate operations practice and that which seeks to apply theoretical concepts into a practical context.