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My co-author Dennis Simon and I studied the success of female candidates running for U.S. Congress for 10 years. Some miscoded data, however, suggested that we had overlooked a major outlet for women's political participation: third parties. Traditionally, research on American elections excludes third-party candidates; when election data are coded, only the results for the two major parties are counted. But as we went further back in time, we kept finding more and more women who had run as third-party candidates and decided this phenomenon was worth investigating. However, we had no idea if there was any kind of relationship between the frequency of women running under third-party labels and women running as Democrats and Republicans: Did fluctuations in one precede the other? Or was there no relationship at all? Because we had time-series data--and competing theories about the possible relationships between them--we used a vector autoregression model and Granger causality tests to explore all the possible ways in which the proportions of women running as major-party candidates and third-party candidates might affect each other.

With the most comprehensive data that exists on women and congressional elections, Palmer and Simon explore the American ""political glass ceiling"" - how incumbency, strategy and redistricting effect the success of women candidates for Congress.

AbstractRecent years have seen a flourishing of Natural Language Processing models that can mimic many aspects of human language fluency. These models harness a simple, decades‐old idea: It is possible to learn a lot about word meanings just from exposure to language, because words similar in meaning are used in language in similar ways. The successes of these models raise the intriguing possibility that exposure to word use in language also shapes the word knowledge that children amass during development. However, this possibility is strongly challenged by the fact that models use language input and learning mechanisms that may be unavailable to children. Across three studies, we found that unrealistically complex input and learning mechanisms are unnecessary. Instead, simple regularities of word use in children's language input that they have the capacity to learn can foster knowledge about word meanings. Thus, exposure to language may play a simple but powerful role in children's growing word knowledge. A video abstract of this article can be viewed at https://youtu.be/dT83dmMffnM.Research Highlights
Natural Language Processing (NLP) models can learn that words are similar in meaning from higher‐order statistical regularities of word use.
Unlike NLP models, infants and children may primarily learn only simple co‐occurrences between words.
We show that infants' and children's language input is rich in simple co‐occurrence that can support learning similarities in meaning between words.
We find that simple co‐occurrences can explain infants' and children's knowledge that words are similar in meaning.

The nature of the COVID-19 pandemic may require governments to use privacy-encroaching technologies to help contain its spread. One technology involves co-location tracking through mobile Wi-Fi, GPS, and Bluetooth to permit health agencies to monitor people's contact with each other, thereby triggering targeted social-distancing when a person turns out to be infected. The effectiveness of tracking relies on the willingness of the population to support such privacy encroaching measures. We report the results of two large surveys in the United Kingdom, conducted during the peak of the pandemic, that probe people's attitudes towards various tracking technologies. The results show that by and large there is widespread acceptance for co-location tracking. Acceptance increases when the measures are explicitly time-limited and come with opt-out clauses or other assurances of privacy. Another possible future technology to control the pandemic involves "immunity passports", which could be issued to people who carry antibodies for the COVID-19 virus, potentially implying that they are immune and therefore unable to spread the virus to other people. Immunity passports have been considered as a potential future step to manage the pandemic. We probe people's attitudes towards immunity passports and find considerable support overall, although around 20% of the public strongly oppose passports.

AbstractHealth data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how "traditional" health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

In response to the COVID-19 pandemic, many Governments are instituting mobile tracking technologies to perform rapid contact tracing. However, these technologies are only effective if the public is willing to use them, implying that their perceived public health benefits must outweigh personal concerns over privacy and security. The Australian federal government recently launched the "COVIDSafe" app, designed to anonymously register nearby contacts. If a contact later identifies as infected with COVID-19, health department officials can rapidly followup with their registered contacts to stop the virus' spread. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google's Bluetooth exposure notification system) in two representative samples of the Australian public prior to the launch of COVIDSafe. We compared these attitudes to usage of the COVIDSafe app after its launch in a further two representative samples of the Australian public. Using Bayesian methods, we find widespread acceptance for all tracking technologies, however, observe a large intention-behaviour gap between people's stated attitudes and actual uptake of the COVIDSafe app. We consider the policy implications of these results for Australia and the world at large.

Taiwan has been successful in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic; however, without a vaccine the threat of a second outbreak remains. Young adults who show few to no symptoms when infected have been identified in many countries as driving the virus' spread through unidentifiable community transmission. Mobile tracking technologies register nearby contacts of a user and notifies them if one later tests positive to the virus, potentially solving this issue; however, the effectiveness of these technologies depends on their acceptance by the public. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google's Bluetooth exposure notification system) among four samples of young Taiwanese adults (aged 25 years or younger). Using Bayesian methods, we find high acceptance for all three tracking technologies (>75%), with acceptance for each technology surpassing 90% if additional privacy measures were included. We consider the policy implications of these results for Taiwan and similar cultures.