Metric forensic anthropology decisions: Reliability and biasability of sectioning-point-based sex estimates

Subjective decisions make human cognitive processes more susceptible to bias and error. Specifically, research indicates that additional context biases forensic anthropologists’ morphological analyses. To address whether metric analyses are also subject to bias, we conducted a pilot study in which 52 experienced osteologists measured a difficult-to-classify human femur, with or without additional contextual information. Using a metric sectioning-point sex-estimation method, participants provided a sex estimate for individual skeletal element(s) and, when given multiple elements, the combined skeletal assemblage. Control group participants (n = 24) measured only the femur. In addition to the femur, bias group participants (n = 28) either measured a female humerus and viewed a female-biasing photograph (n = 14) or measured a male humerus and viewed a male-biasing photograph (n = 14). We explored whether the experts in the different groups would differ in: (1) femoral measurements; (2) femoral sex-estimation conclusions; and (3) final sex-estimation conclusions for the skeletal assemblage. Although the femoral measurements and femoral sex estimates were comparable across groups, the overall sex estimates in the female-biased group were impacted by contextual information—differing from both the control and male-biased groups (p < 0.001). Our results demonstrate that cognitive bias can occur even in metric sex-estimation conclusions. Specifically, this occurred when the metric data and single-element sex estimates were synthesized into an overall estimate. Thus, our results suggest that metric methods are most vulnerable to bias when data are synthesized into an overall conclusion, highlighting the need for bias countermeasures and comprehensive statistical frameworks for synthesizing metric data to mitigate the effects of cognitive bias.     

What do forensic analysts consider relevant to their decision making?

In response to research demonstrating that irrelevant contextual information can bias forensic science analyses, authorities have increasingly urged laboratories to limit analysts' access to irrelevant and potentially biasing information (Dror and Cole (2010) [3]; National Academy of Sciences (2009) [18]; President's Council of Advisors on Science and Technology (2016) [22]; UK Forensic Science Regulator (2015) [26]). However, a great challenge in implementing this reform is determining which information is task-relevant and which is task-irrelevant. In the current study, we surveyed 183 forensic analysts to examine what they consider relevant versus irrelevant in their forensic analyses. Results revealed that analysts generally do not regard information regarding the suspect or victim as essential to their analytic tasks. However, there was significant variability among analysts within and between disciplines. Findings suggest that forensic science disciplines need to agree on what they regard as task-relevant before context management procedures can be properly implemented. The lack of consensus about what is relevant information not only leaves room for biasing information, but also reveals foundational gaps in what analysts consider crucial in forensic decision making.     

Fear of victimization and health

Fear of victimization may have consequences for subjective well-being. I develop and test a model linking fear of victimization to subjective health. I hypothesize that two processes link fear to subjective health-psychological and behavioral. Specifically, I hypothesize that fear of victimization increases psychological distress, and fear decreases outdoor physical activity, especially walking. High levels of psychological distress and low levels of walking, in turn, are associated with poor self-reported health. I find empirical support for the hypothesized processes in a representative national sample of 2031 adults aged 18 to 90 interviewed by telephone in 1990. The negative association between fear and health is explained largely by psychological distress and walking. However, a significant direct effect remains. I conclude with suggestions for future research linking crime and health, focusing on the need for collecting information on community disorganization. Community context is likely the ultimate exogenous variable-the one that sets in motion the destructive cycle of fear, distress, inactivity, and poor health described here.     

When ADR becomes the law: A review of federal practice

Conclusion With the passage of the Administrative Dispute Resolution Act, the stage was set for innovation and change in federal agencies. Now, part way into the five-year life of the Act, a new administration has the potential to encourage even wider use of ADR at the federal level, providing still more examples from which to develop a clearer sense of best practice. Additional funds, both for ACUS and individual agencies, are vital to providing the level of experimentation, innovation, and documentation needed to ensure success.MIT ProfessorLawrence E. Susskind is director of the MIT-Harvard Public Disputes Program, 512 Pound Hall, Harvard Law School, Cambridge, Mass. 02138.Eileen F. Babbitt is associate director of the Public Disputes Program.Phyllis N. Segal is a practicing mediator with Endispute, Incorporated.The authors would like to acknowledge the federal dispute resolution specialists who contributed greatly to our work by providing interviews and materials for this article: Cathy Costantino of FDIC; David Batson of EPA; John Settle and Ron Walczak of HHS; David Drabkin of DLA; Jim Jones of DOL; Sheldon Guttman of FCC; Charles Pou of ACUS; Jeff Domber of GSA; and Diane Liff of DOT.     

Changes in Legislative Support for the Governor's Program Over Time

Abstract This study examines changes in legislative support for the governor's legislative agenda in Georgia during the governor's first term in office (1991–94). I analyze the factors that led legislators to support the governor's agenda, as well as how the level of support changed between election years and off‐years. I use multivariate OLS models of gubernatorial support to determine how support varied (1) between the parties, (2) between factions within parties, and (3) over time. I find that there was wide variation in support among factions in the majority party and that support varied widely between election years and off‐years.