In three experiments we investigated the locus of the frequency effect in lexical access and the mechanism of gender feature selection. In Experiment 1, participants were asked to produce gender-marked verb plus pronominal clitic utterances in Italian (e.g., ‘‘portalo’’ (bring it [masculine]) in response to a written verb and pictured object. We found that pronominal clitic production is sensit...

Decomposing the Gender Wage Gap in the Netherlands with Sample Selection Adjustments In this paper, we use quantile regression decomposition methods to analyze the gender gap between men and women who work full time in the Netherlands. Because the fraction of women working full time in the Netherlands is quite low, sample selection is a serious issue. In addition to shedding light on the source...

Until recently, analyses of gender-dependent differences in viability selection and the ontogeny of sexual size dimorphism have been plagued by difficulties in determining the sex of nestling birds on the basis of morphology. Recently, this problem was overcome using molecular sex identification to report for the first time body-size-mediated antagonistic selection on the viability of male and ...

Business is demanding higher recognition accuracy with no increase in computation time compared to previously adopted baseline speech recognition systems. Accuracy can be improved by adding a gender dependent acoustic model and unsupervised adaptation based on CMLLR (Constrained Maximum Likelihood Linear Regression). CMLLR-based batch-type unsupervised adaptation estimates a single global trans...

Abstract Evidence of female-favoring hiring preferences for assistant professorships suggests that universities can implement affirmative action programs successfully. However, research on the role applicant gender and actual use policies in processes high-level remain scarce. A web-based experiment with 481 economic university members assessed whether evaluators perceived a female as less qual...

We consider the problem of gender classification from frontal facial images using feature selection and neural networks. We argue that feature selection is an important issue in gender classification and we demonstrate that by removing features that do not encode important gender information from the image representation of faces, the error rate can be reduced significantly. Automatic feature s...