Need-efficiency trade-offs in the allocation of resources: Ideological and attributional differences in public aid decision making

We examined whether ideological differences influenced perceptions of the underlying causes of public aid applicants’ predicaments, and whether in turn ideology-patterned attributions accounted for how resource allocators prioritized need- and efficiency-related goals in allocating aid. To examine the need-efficiency trade-off, participants (N=112) divided a hypothetical pool of aid applicants for subsidized health insurance into two “incorrect” allocation outcomes: false alarms (allocate aid to unneedy applicants) and misses (deny aid to needy applicants). Moreover, to examine beliefs about the absolute percentage of aid applicants who are truly in need of societal assistance, participants divided the remaining aid applicants into two “correct” allocation outcomes: hits (allocate aid to needy applicants) and correct rejections (deny aid to unneedy applicants). Results of a series of structural equation models indicated that conservatism was linked to the causal belief that aid applicants’ predicaments arise from dispositional rather than situational factors, which in turn predicted a preference for making efficiency-related over need-related resource allocation judgments (e.g., preferring misses to false alarms) and the belief that a relatively small number of aid applicants are truly in need of societal assistance (e.g., preferring correct rejections to hits). Results are discussed in terms of how ideologically driven attributions influence the manner in which people resolve need-efficiency trade-offs inherent in the context of public aid decision making.     

Elevated and similar urinary testosterone/epitestosterone ratio in all samples of a competition testing: suspicion of a manipulation

The case of seven urine samples collected for anti-doping purposes during a cycling stage race with moderately elevated testosterone and epitestosterone ratio (T/E) is reported. The very low probability of having all seven urine samples with such similar elevated T/E ratio (from 3.2 to 4.7) was very suspicious. Different pattern classification tools were tested to categorize the most similar steroid profiles, but none of the models enabled a clear classification of the different urine samples. Subsequently, genetic profiling of all urine samples was performed and demonstrated that three of the seven samples were collected from the same cyclist. Finally, the International Federation confirmed DNA profiling results. This suggests that urinary steroid data using several methodologies are not appropriate for identification purposes and to an extent not unique to individuals.