A Comparison of Forensic and Nonforensic Malingerers: A Prototypical Analysis of Explanatory Models

Explanatory models of malingering strive to understand the primary motivation underlying attempts to feign. Rogers, Sewell, and Goldstein (1994) provided empirical support for the conceptualization of pathogenic, criminological, and adaptational models. In the current study, a prototypical analysis of 221 forensic experts results in a slightly refined formulation: the adaptational model was decomposed into its two broad dimensions (cost–benefit analysis and adversarial setting). An important finding is that the factor structure for the explanatory models remained stable when applied to both forensic and nonforensic cases. As a first investigation, significant differences were observed in prototypical cases of malingering by the category of referral (forensic or nonforensic) and by type of feigning (mental disorders, cognitive impairment, and medical syndromes). Surprisingly, the feigning of medical syndromes appeared to play a relatively prominent role in both forensic and nonforensic cases and to be influenced by the apparent adversarial context of the assessment. Finally, important gender differences were observed, especially with nonforensic prototypical cases of malingering.     

Factors influencing adoption of automation at wastewater treatment plants in Japan

Japan stands among several nations in the forefront of applying advanced computer technologies to the automation of wastewater treatment plant operations. Three case studies of implementation of innovative information technologies at Japanese sewage treatment plants are presented. Analysis of the cases demonstrates how sociocultural factors, including personal and professional networks, long-term relationships, lifetime employment, and a sense of social responsibility, have contributed to Japan's ability to transfer new computer technologies from research environments to municipal wastewater treatment plants. Srinija Srinivasan, a graduate of Stanford University's Symbolic Systems Program, is a researcher with Microelectronics and Computer Technology Corporation. She and her colleagues are working on Cyc, a large-scale artificial intelligence project to encode common-sense knowledge.