Book bans in political context: Evidence from US schools.

In the 2021-2022 school year, more books were banned in US school districts than in any previous year. Book banning and other forms of information censorship have serious implications for democratic processes, and censorship has become a central theme of partisan political rhetoric in the United States. However, there is little empirical work on the exact content, predictors of, and repercussions of this rise in book bans. Using a comprehensive dataset of 2,532 bans that occurred during the 2021-2022 school year from PEN America, combined with county-level administrative data, multiple book-level digital trace datasets, restricted-use book sales data, and a new crowd-sourced dataset of author demographic information, we find that (i) banned books are disproportionately written by people of color and feature characters of color, both fictional and historical, in children's books; (ii) right-leaning counties that have become less conservative over time are more likely to ban books than neighboring counties; and (iii) national and state levels of interest in books are largely unaffected after they are banned. Together, these results suggest that rather than serving primarily as a censorship tactic, book banning in this recent US context, targeted at low-interest children's books featuring diverse characters, is more similar to symbolic political action to galvanize shrinking voting blocs.

Gender and retention patterns among U.S. faculty.

Women remain underrepresented among faculty in nearly all academic fields. Using a census of 245,270 tenure-track and tenured professors at United States-based PhD-granting departments, we show that women leave academia overall at higher rates than men at every career age, in large part because of strongly gendered attrition at lower-prestige institutions, in non-STEM fields, and among tenured faculty. A large-scale survey of the same faculty indicates that the reasons faculty leave are gendered, even for institutions, fields, and career ages in which retention rates are not. Women are more likely than men to feel pushed from their jobs and less likely to feel pulled toward better opportunities, and women leave or consider leaving because of workplace climate more often than work-life balance. These results quantify the systemic nature of gendered faculty retention; contextualize its relationship with career age, institutional prestige, and field; and highlight the importance of understanding the gendered reasons for attrition rather than focusing on rates alone.

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices.

Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate inference of natural language processing applications. We demonstrate a path to software-equivalent accuracy for the GLUE benchmark on BERT (Bidirectional Encoder Representations from Transformers), by combining noise-aware training to combat inherent PCM drift and noise sources, together with reduced-precision digital attention-block computation down to INT6.