The Consolidation of the White Southern Congressional Vote

This article explores the initial desertion and continued realignment of about one-sixth of the white voters in the South who, until 1994, stood by Democratic congressional candidates even as they voted for Republican presidential nominees. Prior to 1994, a sizable share of the white electorate distinguished between Democratic congressional and presidential candidates; since 1994 that distinction has been swept away. In 1992, a majority of white southern voters was casting their ballot for the Democratic House nominee; by 1994, the situation was reversed and 64 percent cast their ballot for the Republican. Virtually all categories of voters increased their support of Republican congressional candidates in 1994 and the following elections further cement GOP congressional support in the South. Subsequent elections are largely exercises in partisanship, as the congressional votes mirror party preferences. Republicans pull nearly all GOP identifiers, most independents, and a sizeable minority of Democratic identifiers. Democrats running for Congress no longer convince voters that they are different from their party’s presidential standard bearers—a group that has consistently been judged unacceptable to overwhelming proportions of the southern white electorate.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

The higher education impact agenda, scientific realism and policy change: the case of electoral integrity in Britain

Pressures have increasingly been put upon social scientists to prove their economic, cultural and social value through ‘impact agendas’ in higher education. There has been little conceptual and empirical discussion of the challenges involved in achieving impact and the dangers of evaluating it, however. This article argues that a critical realist approach to social science can help to identify some of these key challenges and the institutional incompatibilities between impact regimes and university research in free societies. These incompatibilities are brought out through an autobiographical ‘insider-account’ of trying to achieve impact in the field of electoral integrity in Britain. The article argues that there is a more complex relationship between research and the real world which means that the nature of knowledge might change as it becomes known by reflexive agents. Secondly, the researchers are joined into social relations with a variety of actors, including those who might be the object of study in their research. Researchers are often weakly positioned in these relations. Some forms of impact, such as achieving policy change, are therefore exceptionally difficult as they are dependent on other actors. Strategies for trying to achieve impact are drawn out such as collaborating with civil society groups and parliamentarians to lobby for policy change

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

A randomized, double-blind, placebo-controlled pilot study of naltrexone in outpatients with bipolar disorder and alcohol dependence

10.1111/j.1530-0277.2009.01024.xAlcoholism: Clinical and Experimental Research33111863-1869ACRS