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Default opt-ins and social proof tags effect on decision making in an e-commerce context

Here the authors examined the relationship between education levels and the gender wage gap (GWG) in the US and Puerto Rico from 2010 to 2022, hypothesizing that higher education would correlate with a lower GWG. Their analysis of income data revealed an inverse correlation, where higher education levels were associated with reduced gender wage disparities, suggesting that policies aimed at closing the gender gap in higher education could promote socioeconomic equality.

The authors explored the effects of the Mount Laurel Doctrine on housing affordability, unemployment rate, and civilian labor force in Burlington County, New Jersey compared to nearby counties.

The authors assessed the degree of women participation in India's economy as a way to estimate woman's participation in India's economic growth.

Here, seeking to better understand how facial features provide important visual cues to help convey emotions, the authors evaluated the accuracy and reaction time of participants in regards to experimental photographs where a person's eyebrows were obscured and ones where they were not. Their findings revealed that removing eyebrows resulted in a significant decrease in a participant's ability to recognize anger, with adolescents most likely to misidentify emotions.

The authors analyze differences in sports commentary between men's matches and women's matches during the 2023 US Open.

The authors investigate the relationship between self-esteem, social desirability, and emotion regulation in children and adolescents.

The authors explore factors affecting graduation rates of students learning English as a second language across Florida counties.

Debates around legacy preferences, recruited athletes, and affirmative action in U.S. college admissions often focus on the belief that graduating from a more selective institution leads to higher future earnings. The study hypothesized a positive correlation between college selectivity and future income due to enhanced resources and opportunities.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.