Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. Here we assess the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years. We find that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing. We show that this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies. To further explain our findings, we examine co-authorship networks and find that while the network has become markedly more integrated in general, the current degree of segregation by race/ethnicity is greater now than it has been in the past. Citing further from oneself on the network is associated with greater balance, but White authors' preferential citation of White authors remains even at high levels of network exploration. We also quantify the effects of intersecting identities, determining the relative costs of gender and race/ethnicity, and their combination in women of color. Our findings represent a call to scientists and journal editors of all disciplines to consider the ethics of citation practices, and actions to be taken in support of an equitable future.
We receive bits of information every day. They come to us in a stream. When we listen to music, read a book, or solve a math problem we receive a stream of musical bits, word bits, or math bits. Our minds arrange that stream into a network. A network links together bits of information like musical notes, syllables, or math concepts. Networks help us to organize information and anticipate what is coming next. In this article, we ask two questions about how our minds build networks: First, are some networks easier to learn than others? And second, do we find some links between bits of information more surprising than others? The answer to both questions is “yes.” The findings reveal how humans learn about the networked world around them. Knowing how humans learn can also help us understand how to teach in ways that will result in the best learning.
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