Justice and emotional reactions to the disadvantaged

Various attitutidinal, cognitive, emotional, and actional reactions to problems and needs of less fortunate people (unemployed, poor people in the developing countries, foreign workers in West Germany) were assessed in a questionnaire study with 865 respondents. The external validity of self-report data was established by external ratings. The focus was on emotional reactions (existential guilt, sympathy, moral outrage because of unjust disadvantages, anger about the disadvantaged, contentment with one's own advantages, fear of losing these, hopelessness with respect to the fate of the less fortunate). Several justice-related variables (beliefs, views, appraisals) as well as responsibility-related variables and social attitudes were assessed as predictors of emotions. The importance of justice-related variables for the arousal of different social emotions was clearly shown. The use and usefulness of cognitive models of discrete emotions is discussed. The impact of emotions on the readiness to various forms of prosocial activities in favor of the less fortunate was also shown: Moral outrage and existential guilt proved to be much more salient predictors than sympathy. Crucial differences between these three prosocial emotions as well as the impact of justice-related variables on readiness to prosocial activities are discussed.     

The measurement of specialization and escalation in the criminal career: An alternative modeling strategy

Recent research using indices of specialization and escalation, such as the forward specialization coefficient and the escalation coefficient, have generally shown that repeat offenders tend to commit the same type or a more serious type of crime on successive arrests. Unfortunately, there are two important limitations to the use of specialization and escalation indices: (1) the meaning and interpretation of the coefficients is often unclear, and (2) the coefficients cannot be tested for statistical significance across groups. In an attempt to account for these limitations and to extend prior research in this area, this paper applies a class of log-linear models developed for studying social mobility tables with matched categories (for one or more groups) to crime-type switching tables. The benefits of using these models, in comparison with prior specialization and escalation research, are that the parameter estimates can be interpreted directly as tests of specialization and escalation in a meaningful way and the parameter estimates can be tested for equality across groups, such as age, race, and gender. The application and interpretation of these models are illustrated with arrest data from a sample of felony offenders in Michigan.     

Surface Curvature of Pelvic Joints from Three Laser Scanners: Separating Anatomy from Measurement Error

Recent studies have reported that quantifying symphyseal and auricular surface curvature changes on 3D models acquired by laser scanners has a potential for age estimation. However, no tests have been carried out to evaluate the repeatability of the results between different laser scanners. 3D models of the two pelvic joints were generated using three laser scanners (Custom, Faro, and Minolta). The surface curvature, the surface area, and the distance between co‐registered meshes were investigated. Close results were found for surface areas (differences between 0.3% and 2.4%) and for distance deviations (average <20 μm, SD <200 μm). The curvature values were found to be systematically biased between different laser scanners, but still showing similar trends with increasing phases/scores. Applying a smoothing factor to the 3D models, it was possible to separate anatomy from the measurement error of each instrument, so that similar curvature values could be obtained (p < 0.05) independent of the specific laser scanner.     

Gender Determination of Adult Individuals by Three‐Dimensional Modeling of Canines

Gender determination is a fundamental issue in forensic anthropology. Many techniques based on bone and dental remains have been proposed. It is not always possible to implement the techniques using bones, but teeth are often perfectly preserved. It has been demonstrated that the canine has the greatest sexual dimorphism, and the aim of this work was to provide an easy and accurate dental technique for determining the gender in the absence of other skeletal elements. The sample was composed of 210 CT scans with four healthy canines. The 840 canines were modeled using MIMICS^® 10.01 software. The total volume of each tooth was determined. Seven mathematical models were determined by binary logistic regressions and ranked in order of relative performance. The seven proposed predictive models thus performed (0.910 ≤ AUC ≤ 0.938), with overall rates of correct predictions between 82.38 and 85.24%. The 4‐canine model is the most powerful for predicting the gender.     

Off-Label Prescribing by Psychiatrists: What is the Practitioner's Liability?

In psychiatry, the molecules available and the dosages recommended when a drug receives marketing authorization are not always adequate to treat patients with major behavioral disturbances. Off-label prescribing is frequent in this context, with regard to the indications and the dosages given as well as to the drug combinations used. However, if complications or death occur, the practitioner's liability may be engaged. The authors report three deaths attributed to off-label prescribing in psychiatry and which led to charges against the physicians. They review the precautions to be taken when prescribing in such conditions (no other possible treatment, existence of sound scientific evidence, consent obtained from the patient, or their legal representatives except in cases of force majeure) and the physician's liability if adverse events occur that could be attributed to off-label prescribing.     

IDENTIFYING MECHANISMS BEHIND POLICY INTERVENTIONS VIA CAUSAL MEDIATION ANALYSIS

Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medical sciences in recent years. The framework enables us to show exactly what assumptions are sufficient for identifying causal mediation effects for the mechanisms of interest, derive a general algorithm for estimating such mechanism‐specific effects, and formulate a sensitivity analysis for the violation of those identification assumptions. We also discuss an extension of the framework to analyze causal mechanisms in the presence of treatment noncompliance, a common problem in randomized evaluation studies. The methods are illustrated via applications to two intervention studies on pre‐school classes and job‐training workshops.     

The Contingency of Voter Learning: How Election Debates Influenced Voters’ Ability and Accuracy to Position Parties in the 2010 Dutch Election Campaign

Election campaigns are expected to inform voters about parties’ issue positions, thereby increasing voters’ ability to influence future policy and thus enhancing the practice of democratic government. We argue that campaign learning is not only contingent on voters’ characteristics and different sources of information, but also on how parties communicate their issue positions in election debates. We combine a two-wave panel survey with content analysis data of three televised election debates. In cross-classified multilevel auto-regression models we examine the influence of these debates in the 2010 Dutch parliamentary election campaign on voters’ knowledge of the positions of eight parties on three issues. The Dutch multiparty system allows us to separate voters’ ability to position parties from their accuracy in ordering these parties. We reach three main conclusions. First, this study shows that voters become more able and accurate during the campaign. However, these campaign learning effects erode after the elections. Second, whereas voters’ attention to campaigns consistently contributes to their ability to position parties, its effect on accuracy is somewhat less consistent. Third, televised election debates contribute to what voters learn. Parties that advocate their issue positions in the debates stimulate debate viewers’ ability to position these parties on these issues. In the face of the complexity of campaigns and debates in multiparty systems, campaigns are more likely to boost voters’ subjective ability to position parties than their accuracy.     

Assessing the Influence of Political Parties on Public Opinion: The Challenge from Pretreatment Effects

Despite generations of research, political scientists have trouble pinpointing the influence of political parties on public opinion. Recently, scholars have made headway in exploring whether parties in fact shape policy preferences by relying on experimental designs. Yet, the evidence from this work is mixed. I argue that the typical experiment faces a design problem that likely minimizes the extent to which parties apparently matter. Because parties have policy reputations, experimental participants may already know from real-world exposure to political debate where the parties stand before they are told in the experiment—they are “pretreated.” This study investigates how real-world political context interferes with party cue stimulus in experiments. In two experiments I show that two types of “pretreatment” from outside the experiment—exposure-based and reputation-based—dramatically moderate the effects of party cues in experiments. Moreover, the politically aware participants—who are most likely to have been pretreated before entering the experiment—are the most sensitive to this interference from real-world context. Paradoxically, experimenters are most likely to find no effect of parties at the very time that their influence is strongest outside the experiment. These findings emphasize the importance of keeping real-world context in mind when designing and analyzing experiments on political communication effects and might help reconcile disparate results of previous party cue experiments.     

CRIME DIVERSITY*

Large geographic areas should host a greater diversity of crime compared with small geographic areas. This proposition is reasonable given that larger geographic areas should not only support more crime but also contain a greater diversity of criminogenic settings. This article uses a neutral model to characterize crime richness as a function of area. The model starts with two neutral assumptions: 1) that all environments are statistically equivalent and exert no influence on what types of crimes occur there; and 2) that different crime types occur independently of one another. The model produces rigorous predictions for the mean and variance in crime richness with increasing area. Tests of the model against a sample of 172,055 crimes occurring in Los Angeles during the year 2013 are qualitatively consistent with neutral expectations. The model is made quantitatively consistent by constant scaling. Resampling experiments show that at most 20 percent of the mean crime richness is attributable to nonrandom clustering and assortment of crime types. A modified neutral model allowing for variation crime concentration is consistent with observed variance in crime richness. The results suggest that very general and largely neutral laws may be driving crime diversity in space.     

Economic and Sociological Correlates of Suicides: Multilevel Analysis of the Time Series Data in the United Kingdom

For the effects of social integration on suicides, there have been different and even contradictive conclusions. In this study, the selected economic and social risks of suicide for different age groups and genders in the United Kingdom were identified and the effects were estimated by the multilevel time series analyses. To our knowledge, there exist no previous studies that estimated a dynamic model of suicides on the time series data together with multilevel analysis and autoregressive distributed lags. The investigation indicated that unemployment rate, inflation rate, and divorce rate are all significantly and positively related to the national suicide rates in the United Kingdom from 1981 to 2011. Furthermore, the suicide rates of almost all groups above 40 years are significantly associated with the risk factors of unemployment and inflation rate, in comparison with the younger groups.