Who Registers? Village Networks, Household Dynamics, and Voter Registration in Rural Uganda

Who registers to vote? Although extensive research has examined the question of who votes, our understanding of the determinants of political participation will be limited until we know who is missing from the voter register. Studying voter registration in lower-income settings is particularly challenging due to data constraints. We link the official voter register with a complete social network census of 16 villages to analyze the correlates of voter registration in rural Uganda, examining the role of individual-level attributes and social ties. We find evidence that social ties are important for explaining registration status within and across households. Village leaders—and through them, household heads—play an important role in explaining the registration status of others in the village, suggesting a diffuse process of social influence. Socioeconomic factors such as income and education do not explain registration in this setting. Together these findings suggest an alternate theory of participation is required.

Assessing uncertainty in past ice and climate evolution: overview, stepping-stones, and challenges

In the geosciences, complex computational models have become a common tool for making statements about past earth system evolution. However, the relationship between model output and the actual earth system (or component thereof) is generally poorly specified and even more poorly assessed. This is especially challenging for the paleo sciences for which data constraints are sparse and have large uncertainties. Bayesian inference offers a self-consistent and rigorous framework for assessing this relationship as well as a coherent approach to combining data constraints with computational modelling. Though “Bayesian” is becoming more common in paleoclimate and paleo ice sheet publications, our impression is that most scientists in these fields have little understanding of what this actually means nor are they able to evaluate the quality of such inference. This is especially unfortunate given the correspondence between Bayesian inference and the classical concept of the scientific method. Herein, we examine the relationship between a complex model and a system of interest, or in equivalent words (from a statistical perspective), how uncertainties describing this relationship can be assessed and accounted for in a principled and coherent manner. By way of a simple example, we show how inference can be severely broken if uncertainties are erroneously assessed. We explain and decompose Bayes Rule (more commonly known as Bayes Theorem), examine key components of Bayesian inference, offer some more robust and easier to attain stepping stones, and provide suggestions on implementation and how the community can move forward. This overview is intended for all interested in making and/or evaluating inferences about the past evolution of the Earth system (or any of its components), with a nominal focus on past ice sheet and climate evolution during the Quaternary.

Using Drifter Velocity Measurements to Assess and Constrain Coarse-Resolution Ocean Models

Properly fitting ocean models to observations is crucial for improving model performance and understanding ocean dynamics. Near-surface velocity measurements from the Global Drifter Program (GDP) contain valuable information about upper ocean circulation and air-sea fluxes on various space and time scales. This study explores whether GDP measurements can be used for usefully constraining the surface circulation from coarse-resolution ocean models, using global solutions produced by the consortium for Estimating the Circulation and Climate of the Ocean (ECCO) as an example. To address this problem, a careful examination of velocity data errors is required. Comparisons between an ECCO model simulation, performed without any data constraints, and GDP and Ocean Surface Current Analyses Real-time (OSCAR) velocity data, over the period 1992–2017, reveal considerable differences in magnitude and pattern. These comparisons are used to estimate GDP data errors in the context of the time-mean and time-variable surface circulations. Both instrumental errors and errors associated with limitations in model physics and resolution (representation errors) are considered. Given the estimated model-data differences, errors and signal-to-noise ratios, our results indicate that constraining ocean state estimates to GDP can have a substantial impact on the ECCO large-scale time-mean surface circulation over extensive areas. Impact of GDP data constraints on the ECCO time-variable circulation would be weaker and mainly limited to low latitudes. Representation errors contribute substantially to degrading the data impacts.