A mixed DNA profile controversy revisited

Semaan et al. (J Forensic Res, 2020, 11, 453) discuss a mock case “where eight different individuals [P₁ through P₈] could not be excluded in a mixed DNA analysis. Even though … expert DNA mixture analysis software was used.” Two of these are the true donors. The LRs reported are incorrect due to the incorrect entry of propositions into LRmix Studio. This forced the software to account for most of the alleles as drop-in, resulting in LRs 60–70 orders of magnitude larger than expected. P₁, P₂, P₄, P₅, and P₈ can be manually excluded using peak heights. This has relevance when using LRmix which does not use peak heights. We extend the work using the same two reference genotypes who were the true contributors as Semaan et al. (J Forensic Res, 2020, 11, 453). We simulate three two-donor mixtures with peak heights using these two genotypes and analyze using STRmix?. For the simulated 1:1 mixture, one of the non-donors’ LRs supported him being a contributor when no conditioning was used. When considered in combination with any other potential donors (i.e., with conditioning), this non-donor was correctly eliminated. For the 3:1 mixture, all results correctly supported that the non-donors were not contributors. The low-template 4:1 mixture LRs with no conditioning showed support for all eight profiles as donors. However, the results from pair-wise conditioning showed that only the two ground truth donors had LRs supporting that they were contributors to the mixture. We recommend the use of peak heights and conditioning profiles, as this allows better sensitivity and specificity even when the persons share many alleles.     

Inclusivity in Online Platforms: Recruitment Strategies for Improving Participation of Diverse Sociodemographic Groups

Governments are increasingly implementing smart and digital approaches to promoting citizen participation. However, whether online participation platforms are tools that improve inclusivity in citizen participation remains underexplored. To address this gap, this article focuses on the role of recruitment messages and their effect on participation in an online participation platform by gender and age. A field experiment with a neighborhood census sample (N = 6,066) shows that online participation dips for younger and older citizens and is equal among women and men. For the age groups between 60 and 75, differences in the control and intervention recruitment messages significantly impacted participation. These findings can help public managers tailor recruitment strategies to facilitate inclusive participation and represent a first step toward learning what types of messages are effective for whom.     

Representational Altruism: The Wary Cooperator as Authoritative Decision Maker

What drives policymakers to put the interests of others above their own? If human nature is inherently selfish, it makes sense to institutionalize incentives that counter decision makers' temptations to use their positions to benefit themselves over others. A growing literature rooted in evolutionary theories of human behavior, however, suggests that humans, under certain circumstances, have inherent predispositions towards “representational altruism,” i.e., to make an authoritative decision to benefit another at one's own expense. Drawing on Hibbing and Alford's conception of the wary cooperator, a theoretical case is made for such behavioral expectations, which are confirmed in a series of original laboratory experiments.     

Flipping burgers or flipping pages? Student employment and academic attainment in post-Soviet Latvia

Latvian higher education has undergone a dramatic transition since 1991. This study employs a survey of nearly 1000 social science students studying in 13 different institutions in Latvia to consider the impact of the increase in the number of students who are working while studying. Evidence indicates employment has a strong and significant negative impact on school performance, and the negative impact increases as weekly hours worked increase. This manifests itself through reduced class attendance and reduced time spent in independent study. Finally, we find that the probability of student employment is most significantly affected by the availability of financial aid, gender, ethnicity, and age.