No Arabic abstract
I analyze the postdoctoral career tracks of a nearly-complete sample of astronomers from 28 United States graduate astronomy and astrophysics programs spanning 13 graduating years (N=1063). A majority of both men and women (65% and 66%, respectively) find long-term employment in astronomy or closely-related academic disciplines. No significant difference is observed in the rates at which men and women are hired into these jobs following their PhDs, or in the rates at which they leave the field. Applying a two-outcome survival analysis model to the entire data set, the relative academic hiring probability ratio for women vs. men at a common year post-PhD is H_(F/M) = 1.08 (+0.20, -0.17; 95% CI); the relative leaving probability ratio is L_(F/M) = 1.03 (+0.31, -0.24). These are both consistent with equal outcomes for both genders (H_(F/M) = L_(F/M) = 1) and rule out more than minor gender differences in hiring or in the decision to abandon an academic career. They suggest that despite discrimination and adversity, women scientists are successful at managing the transition between PhD, postdoctoral, and faculty/staff positions.
Gender equity is one of the biggest issues facing the field of astrophysics, and there is broad interest in addressing gender disparities within astronomy. Many studies of these topics have been performed by professional astronomers who are relatively unfamiliar with research in fields such as gender studies and sociology. As a result, they adopt a normative view of gender as a binary choice of male or female, leaving astronomers whose genders do not fit within that model out of such research entirely. Reductive frameworks of gender and an overemphasis on quantification as an indicator of gendered phenomena are harmful to people of marginalized genders, especially those who live at the intersections of multiple axes of marginalization such as race, disability, and socioeconomic status. In order for the astronomy community to best serve its marginalized members as we move into the next decade, a new paradigm must be developed. This paper aims to address the future of gender equity in astronomy by recommending better survey practices and institutional policies based on a more complex approach to gender.
Currently, postdoctoral fellow (PDF) researchers in Canada face challenges due to the precarious nature of their employment and their overall low compensation and benefits coverage. This report presents three themes, written as statements of need, to support an inclusive and thriving PDF community. These themes are the need for better terms of employment and conditions, the need for access to grants by non-permanent research staff, and the need for a sustainable PDF hiring model that considers the outcomes for the PDFs. We make six recommendations: R1. PDFs should be hired and compensated as skilled experts in their areas, not as trainees. R2. Standard PDF hiring practices should be revised to be more inclusive of different life circumstances. - R2.1 Allow PDFs the option of part-time employment. - R2.2 Remove years-since-PhD time limits from PDF jobs. - R2.3 Financially support PDF hires for relocation and visa expenses. R3. CASCA should form a committee to advocate for and provide support to astronomy PDFs in Canada. R4. CASCA should encourage universities to create offices dedicated to their PDFs. R5. PDFs and other PhD-holding term researchers with a host institution should be able to compete for and win grants to self-fund their own research. R6. Astronomy in Canada should hire general-purpose continuing support scientist positions instead of term PDFs to fill project or mission-specific requirements. In short, we ask for prioritization of people over production of papers.
The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elites, a small set of trusted COVID-19 information sources. We take a census of COVID-19 crowdsourced elites in the United States who have received sustained attention on Twitter during the pandemic. Using a mixed methods approach with a panel of Twitter users linked to public U.S. voter registration records, we find that journalists, media outlets, and political accounts have been consistently amplified around COVID-19, while epidemiologists, public health officials, and medical professionals make up only a small portion of all COVID-19 elites on Twitter. We show that COVID-19 elites vary considerably across demographic groups, and that there are notable racial, geographic, and political similarities and disparities between various groups and the demographics of their elites. With this variation in mind, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote timely public health information and mitigate rampant misinformation.
Academic fields exhibit substantial levels of gender segregation. To date, most attempts to explain this persistent global phenomenon have relied on limited cross-sections of data from specific countries, fields, or career stages. Here we used a global longitudinal dataset assembled from profiles on ORCID.org to investigate which characteristics of a field predict gender differences among the academics who leave and join that field. Only two field characteristics consistently predicted such differences: (1) the extent to which a field values raw intellectual talent (brilliance) and (2) whether a field is in Science, Technology, Engineering, and Mathematics (STEM). Women more than men moved away from brilliance-oriented and STEM fields, and men more than women moved toward these fields. Our findings suggest that stereotypes associating brilliance and other STEM-relevant traits with men more than women play a key role in maintaining gender segregation across academia.
Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we show that a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using career recommendation as a case study, we build a fair AI career recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, our results demonstrate we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans.