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We illustrate some of the challenges of credit allocation in science by discussing the Thomas theorem –often seen as the orgin of the “self-fulfilling prophecy” – which, ironically given its subject matter, has been repeatedly cited as the work of W. I. Thomas alone. Thomas’ coauthor and wife, Dorothy Swaine Thomas, has never received the credit she deserved for the discovery. This raises this issue of how biases affect credit allocation in science, since our perception of who deserves credit is reinforced by the Matthew effect. We tend to give disproportionate credit to renowned scientists over unknowns, making coauthoring with eminent scientists risky. Many of these problems arise because credit is allocated collectively in science, based on the community’s perception of who is responsible for a discovery. While that perception is often correct, there are plenty of instances where the community gets it wrong. We describe how a collective credit allocation algorithm, which was created using cocitation patterns, can capture how the community assigns credit and predict who will get credit for a discovery. We then discuss the algorithm’s implications for individual scientists.
In this chapter we define and detail the Matthew effect, exploring the role that status plays in success. We use the absence and presence of Lord Rayleigh’s authorship on a paper to introduce the idea of reputation signaling, and look at how reputation signaling plays out in randomized control experiments. We then discuss the implications of reputation signaling for both single and double-blind review processes. We find that the Matthew effect applies not just to scientists themselves, but also to their papers through a process known as preferential attachment. To see how an author’s reputation affects the impact of her publications, we look at how her citation patterns deviate from what preferential attachment would predict. We also explore the drivers behind the Matthew effect, asking whether status alone dictates outcomes or whether it reflects inherent talent.
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