Affirmative Action Policies in Academic Job Advertisements: Do They Facilitate or Hinder Gender Discrimination in Hiring Processes for Professorships?

Evidence of female-favoring hiring preferences for assistant professorships suggests that universities can implement affirmative action programs successfully. However, research on the role of applicant gender and the actual use of affirmative action policies in hiring processes for high-level professorships remain scarce. A web-based experiment with 481 economic university members assessed whether evaluators perceived a female applicant as less qualified than a male applicant for an associate professorship position when the job advertisement highlighted the university’s commitment to affirmative action (gender-based preferential selection) but not when it solely highlighted its commitment to excellence (non-gender-based selection). Contrary to previous experimental findings that affirmative action would adversely affect female applicants, evaluators perceived the female applicant as more hirable and ranked her first for the job significantly more often than the male candidate. Furthermore, male evaluators had a stronger preference for the female candidate in the gender-based condition than in the non-gender-based condition and a stronger preference for the male candidate in the non-gender-based condition than in the gender-based condition. Overall, the results provide evidence that gender-based preferential selection policies can evoke their intended effect to bring highly qualified women to high-level professorships, especially when being evaluated by non-beneficiaries of these policies, such as men.

A Bayesian framework for modelling the preferential selection process in respondent-driven sampling

In sampling designs that utilize peer recruitment, the sampling process is partially unknown and must be modelled to make inference about the population and estimate standard outcomes like prevalence. We develop a Bayesian model for the recruitment process for respondent-driven sampling (RDS), a network sampling methodology used worldwide to sample hidden populations that are not reachable by conventional sampling techniques, including those at high risk for HIV/AIDS. Current models for the RDS sampling process typically assume that recruitment occurs randomly given the population social network, but this is likely untrue in practice. To model preferential selection on covariates, we develop a sequential two-sided rational choice framework, which allows generative probabilistic network models to be created for the RDS sampling process. In the rational choice framework, members of the population make recruitment and participation choices based on observable nodal and dyadic covariates to maximize their utility given constraints. Inference is made about recruitment preferences given the observed recruitment chain in a Bayesian framework by incorporating the latent utilities and sampling from the joint posterior distribution via Markov chain Monte Carlo. We present simulation results and apply the model to an RDS study of Francophone migrants in Rabat, Morocco.

Maternal-Fetal HLA Compatibility in Uncomplicated and Preeclamptic Naturally Conceived Pregnancies

IntroductionIn pregnancy, the mother and fetus differ in HLA antigens, and yet the maternal immune system generally tolerates the fetus. KIR receptors expressed by maternal uterine NK cells at the maternal-fetal interface directly interact with HLA-C on extravillous trophoblast cells for optimal placental development. In this study, we aimed to determine whether there is a preferential selection for HLA compatibility and specific KIR/HLA-C combinations in uncomplicated and preeclamptic naturally conceived pregnancies compared to what would be expected by chance.MethodsGenotyping for maternal and fetal HLA-A, -B, -C, -DR, and -DQ, and maternal KIR was performed for 451 uncomplicated pregnancies and 77 pregnancies complicated with preeclampsia. The number of HLA antigen (mis)matches between mother and fetus was calculated and compared to expected values obtained by randomization of the HLA haplotype, inherited from the father, over the existing maternal haplotype of the fetuses. A similar methodology was executed for analysis of the KIR/HLA-C data (n=309).ResultsIn uncomplicated pregnancies, the degree of maternal-fetal HLA matching was not different than expected-by-chance values. In preeclamptic pregnancies, the degree of maternal-fetal HLA matching was different in observed compared to expected-by-chance values (p=0.012). More specifically, the degree of maternal-fetal matching of HLA-C was higher in the actual preeclamptic pregnancies than was expected-by-chance (p=0.007). Preeclamptic pregnancies showed an overall tendency towards higher maternal-fetal HLA compatibility, for total HLA matches (p=0.021), HLA class I (p=0.038) and HLA-C (p=0.025) compared to uncomplicated pregnancies.ConclusionThe data suggest that there is no preferential selection of maternal-fetal HLA compatibility in uncomplicated pregnancies. In contrast, increased total HLA, HLA class I and, especially, HLA-C compatibility is associated with preeclampsia, suggestive for a role of HLA mismatches in immune regulation leading to uncomplicated pregnancy.

A mechanism for evolution of the physical concepts network

We suggest an underlying mechanism that governs the growth of a network of concepts, a complex network that reflects the connections between different scientific concepts based on their co-occurrences in publications. To this end, we perform empirical analysis of a network of concepts based on the preprints in physics submitted to the arXiv.org. We calculate the network characteristics and show that they cannot follow as a result of several simple commonly used network growth models. In turn, we suggest that a simultaneous account of two factors, i.e., growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network. Moreover, the observed structure emerges as a synergistic effect of these both factors: each of them alone does not lead to a satisfactory picture.