Experiments and eligibility cutoffs show that voting is habit-forming

Past research has found a correlation between voting and voting again in future elections, but is there a causal link? In new research which uses Get-out-the-vote and voter eligibility cutoff data from across the US, Alexander Coppock and Donald Green find that there is such a link: voting in one election increases the likelihood of voting in a subsequent election by 10 percentage points

Generalizability of heterogeneous treatment effect estimates across samples

The extent to which survey experiments conducted with nonrepresentative convenience samples are generalizable to target populations depends critically on the degree of treatment effect heterogeneity. Recent inquiries have found a strong correspondence between sample average treatment effects estimated in nationally representative experiments and in replication studies conducted with convenience samples. We consider here two possible explanations: low levels of effect heterogeneity or high levels of effect heterogeneity that are unrelated to selection into the convenience sample. We analyze subgroup conditional average treatment effects using 27 original–replication study pairs (encompassing 101,745 individual survey responses) to assess the extent to which subgroup effect estimates generalize. While there are exceptions, the overwhelming pattern that emerges is one of treatment effect homogeneity, providing a partial explanation for strong correspondence across both unconditional and conditional average treatment effect estimates

Positive, Small, Homogeneous, and Durable: Political Persuasion in Response to Information

This dissertation offers a theory of political persuasion rooted in a Bayesian model of information processing. I support this theory with the results of 20 survey experiments, conducted variously on convenience samples and nationally-representative surveys. From these data, I draw four main conclusions. First, when confronted with persuasive messages, individuals update their views in the direction of information. Second, people change their minds about political issues in small increments. Third, persuasion in the direction of information occurs regardless of background characteristics, initial beliefs, or ideological position. Finally, these changes in political attitudes are not ephemeral, in most cases lasting at least 10 days. These findings stand in contrast to the predictions of the attitude polarization literature, which posits that the effects of persuasion attempts will be positive for some, but negative for others. Across these 20 experiments, I was unable to find any evidence of negative effects (defined as attitude changes away from treatment information) for any subgroup defined by standard demographics, prior attitudes, or their intersections. Instead, people appear to update their views in a manner consistent with Bayes' rule, i.e., as a weighted average of prior beliefs and new information

Declaring and Diagnosing Research Designs.

Researchers need to select high-quality research designs and communicate those designs clearly to readers. Both tasks are difficult. We provide a framework for formally "declaring" the analytically relevant features of a research design in a demonstrably complete manner, with applications to qualitative, quantitative, and mixed methods research. The approach to design declaration we describe requires defining a model of the world (M), an inquiry (I), adatastrategy(D), andananswerstrategy(A). Declaration of these features in code provides sufficient information for researchers and readers to use Monte Carlo techniques to diagnose properties such as power, bias, accuracy of qualitative causal inferences, and other "diagnosands." Ex ante declarations can be used to improve designs and facilitate preregistration, analysis, and reconciliation of intended and actual analyses. Ex post declarations are useful for describing, sharing, reanalyzing, and critiquing existing designs. We provide open-source software, DeclareDesign, to implement the proposed approach

Ecology & computer audition: applications of audio technology to monitor organisms and environment

Among the 17 Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13th SDG is a call for action to combat climate change. Moreover, SDGs 14 and 15 claim the protection and conservation of life below water and life on land, respectively. In this work, we provide a literature-founded overview of application areas, in which computer audition – a powerful but in this context so far hardly considered technology, combining audio signal processing and machine intelligence – is employed to monitor our ecosystem with the potential to identify ecologically critical processes or states. We distinguish between applications related to organisms, such as species richness analysis and plant health monitoring, and applications related to the environment, such as melting ice monitoring or wildfire detection. This work positions computer audition in relation to alternative approaches by discussing methodological strengths and limitations, as well as ethical aspects. We conclude with an urgent call to action to the research community for a greater involvement of audio intelligence methodology in future ecosystem monitoring approaches

Ignorance implicatures of modified numerals

Modified numerals, such as at least three and more than five, are known to sometimes give rise to ignorance inferences. However, there is disagreement in the literature regarding the nature of these inferences, their context dependence, and differences between at least and more than. We present a series of experiments which sheds new light on these issues. Our results show that (a) the ignorance inferences of at least are more robust than those of more than, (b) the presence and strength of the ignorance inferences triggered by both at least and more than depends on the question under discussion (QUD), and (c) whether ignorance inferences are detected in a given experimental setting depends partly on the task that participants are asked to perform (e.g., an acceptability task versus an inference task). We offer an Optimality Theoretic account of these findings. In particular, the task effect is captured by assuming that in performing an acceptability task, participants take the speaker’s perspective in order to determine whether an expression is optimal given a certain epistemic state, while in performing an inference task they take the addressee’s perspective in order to determine what the most likely epistemic state of the speaker is given a certain expression. To execute the latter task in a fully rational manner, participants have to perform higher-order reasoning about alternative expressions the speaker could have used. Under the assumption that participants do not always perform such higher-order reasoning but also often resort to so-called unidirectional optimization, the task effect finds a natural explanation. This also allows us to relate our finding to asymmetries between comprehension and production that have been found in language acquisition

A large-scale and PCR-referenced vocal audio dataset for COVID-19

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.Comment: 37 pages, 4 figure

W.E.B. DuBois for the Twenty‐First Century: On Being a Scholar‐Activist in the Digital Era

W.E.B. Du Bois began his work as a scholar-activist at the dawn of the twentieth century, and this paper argues that his example has much to teach contemporary scholar-activists in the twenty-first century. In order to publish The Crisis, the magazine of the activist organization he co-founded, DuBois purchased a printing press. This meant he could own the means of his own knowledge production and foretold both the promise of what it means to be a scholar-activist in the twenty-first century and the limitations built into the current systems of knowledge production. Du Bois was also prophetic when he identified the problem of the twentieth century as “the problem of the color line,” as the focus of both his scholarship and his activism. The forms of systemic white supremacy we face today are both a continuation of a centuries-old dimension of racism in the U.S. and part of an emerging media ecosystem powered by algorithms. The paper explores the challenges of being digital scholar-activists within legacy institutions. It concludes with speculation about what DuBois might do now