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The authors examine the factors that impact a ballet master's career.

Female employment rates in South Korea are far below those of other countries that are members of the Organization for Economic Co-operation and Development. We assessed job satisfaction, job retention, and the underlying factors that impact these variables for both genders and various ages through a survey. Among 291 adult participants (161 women, 130 men) aged 20 to 59, working in various fields, 95% of responders were college graduates. These results suggest that even highly educated women feel more pressure from an innate sense of responsibility and societal perception to care for children than men.

Inherent bias is often the unconscious driver of human behavior, and the first step towards overcoming these biases is our awareness of them. In this article the authors investigate whether race, gender or species affect the choice of charity by middle class Spaniards. Their conclusions serve as a starting point for further studies that could help charities refine their campaigns in light of these biases effectively transcending them or taking advantage of them to improve their fundraising attempts.

This study aims to examine the demographic factors that predict patterns of co-use of alcohol and marijuana in the United States. Significant findings were identified using data from the National Survey on Drug Use and Health (2012-2022), showing that there were significant differences in the prevalence of substance use among demographic groups, with young adults showing the highest co-use of alcohol and marijuana.

Do different physical traits affect teenagers’ initial trust of an unknown person? Would they give greater trust to women and people of similar ethnicity? To test these hypotheses, the authors developed a survey to determine the sets of physical characteristics that affect a person's trustworthiness. They found that gender and expression were the main physical traits associated with how trustworthy an individual looks, while ethnicity was also important.

Social media is widely used and easily accessible for adolescents, it has the potential to increase STEM (Science, Technology, Engineering, and Math) identity in girls. We aimed to investigate the effects of exposure to counter-stereotypical portrayals of women in STEM on social media on the STEM identity of adolescent girls. The study concluded that social media alone may not be an effective tool to increase STEM identity in girls. Social media can still be used as a complementary tool to support and encourage women in STEM, but it should not be relied upon solely to address the gender disparity in STEM fields.

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

End Stage Renal Disease (ESRD) is a growing health concern in the United States. The authors of this study present a study of ESRD incidence over a 32-year period, providing an in-depth look at the contributions of age, race, gender, and underlying medical factors to this disease.

Here, seeking to identify the possible role of sports in helping teenagers navigate the troubles associated with societal changes during a pandemic, the authors surveyed 50 adolescents to collect Beck Depression Inventory scores. They found that 9 out of students with severe depressions did not do sports, while no significant relationship between depressive symptoms and either gender or place of exercise was observed.

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.