The present study explores demographics, pre-college characteristics and multi-year (2003-2013) tracking of a census of 53,077 students who initially declared a STEM major upon entering a research university in Texas and seeks to predict graduation with a STEM and non-STEM degree. Guided by QuantCrit theory, we use multilevel models to determine factors that predicted persistence in any major and factors that predicted persistence in STEM, as well as use marginal effects to explore the intersection of ethnicity, sex, and first-generation status. Results highlight the disparity that exist amongst Black students and their White counterparts with regards to persistence in any major. We also highlight the gap between first-generation White and Black first-generation females and their Asian and International counterparts with regards to persistence in STEM. Implications for future research and practitioners suggest further attention needs to be paid to Black first-generation students.
This research saliently deconstructs the philosophical writing of a white, privileged male by five diverse academic peers by using a methodology of deconstruction to analyze the initial author’s writing. Their reflects on his nascent perspectives address the stages of racism, mea culpa, the relationship between privilege, oppression, and classism, a feminist perspective, binary, and intersectionality. Further analysis connote for the need to deconstruct privilege in a literary context and to develop an autoethnography to fully delve into privilege beyond a superficial and neglectful narrative.
Purpose
This study aims to examine how gender variation in trans identities shape exposure to bias and discrimination. The authors then examine how trans identities intersect with race/ethnicity, education and social class to shape exposure risk to bias, discrimination and harassment in the workplace.
Design/methodology/approach
The authors use data from the 2015 U.S. Transgender Survey with 24,391 trans-identified respondents. To account for the nested nature of trans people in state contexts, the authors use two-level logistic multilevel models. The authors are guided by Puwar’s bodies out of place as the theoretical grounding for this study.
Findings
The authors find significant differences in how trans women and men experience discrimination. The authors also find differences in race, education and social class. Finally, the presence of anti-discrimination policies presents mixed results.
Originality/value
The authors’ analysis reveals important differences in trans workers’ exposure to discrimination based on gender identity, social class, race/ethnicity and policy context, and draws upon a rich and large data set.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.