13 research outputs found

    Characterizing the effect of retractions on scientific careers

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    Retracting academic papers is a fundamental tool of quality control when the validity of papers or the integrity of authors is questioned post-publication. While retractions do not eliminate papers from the record, they have far-reaching consequences for retracted authors and their careers, serving as a visible and permanent signal of potential transgressions. Previous studies have highlighted the adverse effects of retractions on citation counts and coauthors' citations; however, the broader impacts beyond these have not been fully explored. We address this gap leveraging Retraction Watch, the most extensive data set on retractions and link it to Microsoft Academic Graph, a comprehensive data set of scientific publications and their citation networks, and Altmetric that monitors online attention to scientific output. Our investigation focuses on: 1) the likelihood of authors exiting scientific publishing following a retraction, and 2) the evolution of collaboration networks among authors who continue publishing after a retraction. Our empirical analysis reveals that retracted authors, particularly those with less experience, tend to leave scientific publishing in the aftermath of retraction, particularly if their retractions attract widespread attention. We also uncover that retracted authors who remain active in publishing maintain and establish more collaborations compared to their similar non-retracted counterparts. Nevertheless, retracted authors with less than a decade of publishing experience retain less senior, less productive and less impactful coauthors, and gain less senior coauthors post-retraction. Taken together, notwithstanding the indispensable role of retractions in upholding the integrity of the academic community, our findings shed light on the disproportionate impact that retractions impose on early-career authors.Comment: 49 pages, 13 figures, 19 table

    The Population Level Impacts of Differential Fertility Behavior of Parents of Children with Autism

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    Drawing on population level data of exceptional quality (including detailed diagnostic information on the autism status of sibling pairs of over 3 million different mothers), this study confirms that stoppage is the average fertility response to a child born with autism, thereby reducing observed concordance in sibling pairs and leading to potentially biased estimation of genetic contributions to autism etiology. Using a counterfactual framework and applying matching techniques we show, however, that this average effect is composed of very different responses to suspicion of autism depending on birth cohort, the character of the disorder (severe versus less severe), the gender of the child, poverty status, and parental education. This study also sheds light on when parents suspect autism. We find that parents’ fertility behavior changes relative to matched controls very early after the birth of a child who will later be diagnosed with autism

    Prediction of emerging technologies based on analysis of the US patent citation network

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    Abstract The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, an

    Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

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    The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

    Non-transformative climate policy options decrease conservative support for renewable energy in the US

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    Motivated by ongoing partisan division in support of climate change policy, this paper investigates whether, among self-identifying liberals and conservatives, the mere presence of a non-transformative climate policy such as carbon capture and storage (CCS), lowers support for a renewable energy (RE) policy. To interrogate this question, we use a survey experiment asking 2374 respondents about their support for a RE policy when presented with the RE policy alone, and when presented alongside a CCS policy whose funding and implementation leverage independent funding sources. We find that among conservatives, the presence of a CCS policy lowers support for RE. Furthermore, despite the lack of apparent political party cues, when presented with the policy-pair, conservatives tend to view the RE policy in more partisan terms, specifically, less supported by Republicans. Additional experimental conditions with explicit party cues reinforce this interpretation. These findings suggest that the triggering of partisan perceptions even without explicit partisan cues—what we call political anchoring—might be a key impediment to bipartisan support of climate solutions in the U.S. context

    Durable Change in U.S. Urban Mobility Networks, 2019–2022

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    In 2020, the COVID-19 pandemic significantly altered how people move between neighborhoods. Tracking these changes is important because a growing literature demonstrates that mobility networks influence social and environmental exposures that interact directly with urban inequalities. Using four years of weekly smartphone-based mobility data in the 25 largest U.S. cities, we investigate how mobility changed in 2021 and 2022. We measure mobility networks with three previously used indices and introduce a fourth, the Dissimilar Mobility Index, to capture the demographic dissimilarity experienced in a mobility network. We find that although mobility hubs and their associated patterns of segregated mobility returned to pre-pandemic levels in 2021, neighborhood isolation remained depressed until the end of 2022 compared to 2019. Together, these results indicate that despite vaccine availability in 2021, structural changes in urban mobility networks caused by the COVID-19 pandemic were durable for over two years after its onset
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