Our 2020 analysis correctly forecasted Joe Biden’s victory and the outcome of every state except Georgia. That forecast relied on economic data from 125 days prior to Election Day and presidential approval data from 104 days (or more) before the election. Since 2000, our model would have correctly forecasted the winner in 95% of all states. We updated our State Presidential Approval/State Economy Model for 2024. This article summarizes the model and its historical accuracy as well as new data updates. We then generate forecasts for the overall two-party popular vote, each state’s outcome, and the Electoral College winner for the 2024 US presidential election. One hundred days prior to Election Day, our model forecasts a split two-party popular vote (50.3% for Trump, 49.7% for Harris) but a notable Trump advantage in the Electoral College, with slightly less than a three-in-four chance that Trump wins the election. This Republican advantage 100 days prior to Election Day sheds light on Biden’s abrupt decision to drop out of the race and suggests that if Harris wins, she will have overcome extremely challenging fundamentals, and/or that Donald Trump and the Republican Party will have squandered a sizeable Electoral College advantage.

This Special Issue presents a wide array of election forecasting models for the 2024 US elections. Most of these models generate forecasts for the presidential, congressional, and gubernatorial races. The contributions are characterized by the variety of their approaches: citizen forecasting, electronic markets, large language models, machine learning, poll-based models, and regression analysis. This introduction first summarizes some of the lessons and challenges of election forecasting. We then provide a brief context of the 2024 campaign and a short overview of the articles included in the Special Issue. The forecasts point to a tight presidential race. The two-party popular-vote predictions are almost evenly split, with some favoring Donald Trump and others Kamala Harris. However, among the models that offer an Electoral College forecast, three predict that Harris will win and five predict that Trump will return to the presidency.

Despite governors’ crucial roles in shaping important policies, including abortion, education, and infrastructure, forecasters have paid little attention to gubernatorial elections. We posit that institutional idiosyncrasies and lack of public opinion data have exacerbated the classic problem facing all election forecasts: there are too many predictors and too few cases, leading to overfitting. To address these problems, we combine new governor and state-level presidential approval data with a machine-learning approach, LASSO, for variable selection. LASSO examines numerous variables but retains only those that substantively improve model performance. Results demonstrate the efficacy of gubernatorial and presidential approval ratings measured two quarters preelection in predicting both incumbent-party vote share and election winners in out-of-sample predictions. For 2022, our approach outperformed the Cook Political Report’s Partisan Voting Index and compared well with 538’s Election Day prediction. For 2024, our LASSO-Popularity model predictions indicate that it will likely be a difficult year for Democrats in gubernatorial contests.

As more people are displaced by climate change, public acceptance of migrants is an increasingly relevant geographical and political issue. How willing are Americans to accept climate migrants and how does this support compare to others who are fleeing conflict? We conducted a nationally representative survey experiment (N=1,027) with prompts that varied the context of refugee resettlement, including a control condition without context, those displaced by global warming, refugees from Ukraine, and refugees from Afghanistan. Respondents expressed marginally lower willingness to admit climate migrants and significantly higher willingness to admit Ukrainian refugees. These differences were amplified by partisanship, religion, and race. These results suggest that some migrants experience a more welcoming public than others and highlight a challenge for those who are made vulnerable by climate change.

The dramatic impacts of climate change presage an inevitable surge in mass migration; however, advanced democracies are ill-equipped for this impending crisis. Moreover, we know very little about how publics evaluate this group of prospective migrants, who are estimated to increase from 100 million to 200 million worldwide within decades. This study investigates American attitudes toward climate-related migrants in a conjoint experiment of more than 1,000 US adults, in which respondents evaluated fictional refugee profiles that varied across multiple attributes. Findings reveal that Americans (1) prefer political refugees over climate-related refugees; and (2) prefer climate-related and economic migrants to a similar extent, and that these preferences are not driven by concerns over climate-related refugees’ integration into American society. Subgroup analyses indicate that younger individuals, those with high climate-change anxiety, and those who previously engaged in climate-related political activities discriminated less against climate-related migrants. Analyses of open-ended responses reveal that climate anxiety is a driver of positive evaluations of climate-related migrants.

Scientific expertise is crucial for responding effectively to environmental crises. Nevertheless, under conditions of political inequality, expert policy making can inhibit policy solutions by altering incentives of powerful interest groups. This is the situation facing the predominantly Alaska Native communities of the Yukon-Kuskokwim Delta, which have long relied on salmon for subsistence and are now experiencing a collapse of the salmon population. Scientific evidence indicates that climate change is a primary cause, and experts therefore have opposed demands by Native subsistence fishers for ameliorative measures—especially restricting pollock fishing—as likely to be ineffective. However, this approach eliminates incentives for the influential pollock industry to support policies to address the salmon crisis, including climate-change mitigation. This article presents a simple formal model that demonstrates these incentive effects. This argument contributes to theories of business power and shows how expert policy making can inadvertently force marginalized communities to bear the burden of climate change.

The central question guiding this research is whether municipal councils governed by mayors with party membership differ significantly in their transparency from those governed by non-partisan mayors. Although this is an exploratory study that does not seek to test hypotheses, there are ongoing lively theoretical debates about the implications stemming from the decline of traditional parties and partisanship, and the rise of independent local lists (ILLs) (as well as the rise of new challenger parties). Dealing with unbalanced panel data from 308 Portuguese municipal councils from 2013 to 2017, we use a Bayesian hierarchical model based on a Beta regression. Our findings suggest that there are no differences in local transparency levels—as measured by the Municipal Transparency Index—in municipal councils governed by mayors with party membership and those by non-partisan mayors. Given that transparency is a common concern for both party- and non-party–aligned local governments as well as voters, this preliminary analysis asks important substantive questions about the promises that many ILLs make about their ability to clean up party politics and return power to “the people.”

Research has shown that as the size of government assistance programs grow, and the recipients of such programs are increasingly non-white and/or non-citizen, public support for them declines. Our study examines this phenomenon on the question of deservingness in federal disaster assistance. Using a 2018 survey experiment that leverages two devastating hurricanes—Hurricane Maria and Hurricane Harvey—that hit different parts of the United States in 2017, we explore how the social identities of race/ethnicity and partisanship affect attitudes about disaster deservingness. Our results demonstrate that although federal disaster assistance has broad support, it is contingent on perceptions about the disaster victim and the type of assistance. Respondents were less likely to support disaster assistance to Hurricane Maria–affected people than those affected by Hurricane Harvey. Moreover, white and Republican respondents were more likely to favor market-based assistance whereas race-/ethnic-minority and Democratic respondents were more likely to support more generous forms of disaster assistance. These findings have important implications for the allocation of disaster funds as climate change intensifies and the frequency of billion-dollar disaster events increases. This is exacerbated by political polarization and heightened social vulnerability due to changing population demographics.

Understanding why people choose to adapt or emigrate when facing slow-onset climate events is central to the design and implementation of policy addressing displacement spurred by climate change, especially climate-refugee programs. In this article, we leverage extensive field interviews and a novel survey in the Federated States of Micronesia, a country at high risk of environmental degradation but whose citizens have carte blanche access to the United States. We find that despite a general awareness of environmental risks, these play a minor role in migration decision making. Instead, other factors like work, health, and family obligations take precedence.

Climate justice movements and scholars have established that marginalized communities, including people of color, Indigenous Peoples, women, and the Global South, are most vulnerable to climate change. Recently, scholars also have established that the climate crisis places LGBTQ+ communities in precarious positions. Yet, we know little about how LGBTQ+ activists practice climate justice and build political bridges between LGBTQ+ and climate justice movements. By analyzing queer climate activism, I find that bridging the US climate and LGBTQ+ movements share three elements: (1) vulnerability and intersectional analysis, (2) survival and resilience, and (3) play. In bridging the movements, activists “queer” climate justice by spatially shifting on what grounds or issues to fight, prefiguring worlds not yet in existence on a larger scale, and reimagining how to perform climate activism.

Existing literature on climate politics predominantly concentrates on democracies. However, there is a pressing need to examine how authoritarian regimes respond to climate change, given their growing impact on global carbon emissions and their populations’ acute climate vulnerability. Extant research often assumes that authoritarian regimes have inherent advantages in addressing climate change, leading to overly optimistic perspectives on their capabilities. This study highlights the necessity of qualifying those assumptions and evaluates the comparative advantages and disadvantages of autocracies relative to democracies throughout the policy process: policy formulation (or outputs), implementation, and outcomes. I argue that whereas climate-conscious autocracies may efficiently produce policy outputs based on scientific evidence, they often face more challenges related to information about local enforcement during implementation. This may result in greater hurdles than democracies, even with adequate state capacity and monitoring infrastructure. Furthermore, this analysis contends that a country’s developmental stage, rather than its regime type, is related more directly to the effectiveness of translating implementation efforts into tangible policy outcomes. Therefore, this article posits that the political science discourse, which often juxtaposes democracies with autocracies, should expand its scope to better understand how a country’s developmental level influences the success of its climate strategies.

Climate change is a global phenomenon affecting millions of people. Low-income people and communities are particularly at risk because of limited capacities to cope with climate-related stresses. Differential access to social and economic resources determines the level of adaptive potential; thus, the variations in social vulnerability to climate change. This article explores how unequal power relations influence the level of vulnerability in rural agrarian Bangladesh. Using data from the coastal region, it specifically discusses ethnicity, religion, gender, and farm size as the sources and manifestations of power relations in Bangladeshi rural society. I argue that the deeply rooted institutions of power in the country shape access to important resources that might increase adaptive capacity and thus resilience in the context of rapid climate change.

Recent applications of new innovations in artificial intelligence have brought up questions about how this new technology will change the landscape and practices in a wide range of industries and sectors. This article focuses on the impact of generative large language models on teaching, learning, and academic assessment in political science education by analyzing two novel surveys administered by the discipline’s major professional body, the American Political Science Association. We present the results of these surveys and conclude with recommendations.

Service-learning courses help students to identify career opportunities and foster civic engagement, but links to projects with local governments may be difficult to forge. State municipal associations are well positioned as intermediaries to link local governments and higher-education institutions, and their historic roots affirm their capacity to invest in the professionalization of the future public-service workforce. Yet, a recent survey of municipal associations revealed limited contact with higher-education institutions for the purpose of engaging students. Examples from Georgia, Iowa, and Washington highlight the potential role that municipal associations can have in the creation of service-learning opportunities for students. The demand for skilled workers in local government necessitates action by municipal associations and educators in political science to expand service-learning opportunities and access to local government.

Prior research finds that women earn fewer citations than men for their publications, and it offers various reasons why this is the case. This study provides new evidence on these citation differences from two datasets on career citations earned by male and female political scientists. Our findings extend and elaborate on those in earlier research. Most notably, we find that older cohorts of women demonstrate substantial progress toward citation equity with their male peers.

This study reflects on the field research interruptions that occurred around the world with the onset of the COVID-19 pandemic. Based on my experience of in-person and remote fieldwork with vulnerable populations and sensitive research topics during this time, I introduce a “zig-zagging approach” that can be used as a research adaptivity strategy in times of disruption. I argue that “zig-zagging your way through the field” is a legitimate strategy as long as researchers acknowledge that changing from in-person to remote fieldwork (and vice versa) will alter various aspects of their relationship with the field including;(1) perception of positionality and authenticity; (2) processes of trust building and security challenges; and (3) experience of ethnographic immersion and observation. I offer mitigation strategies to reduce the impact of change and also discuss aspects that cannot be mitigated when working with vulnerable populations or sensitive research topics. I conclude on why going back—and forth (i.e., zig-zagging)—should become a practical solution when all else fails.

Academic conferences are important institutions for promoting new research and facilitating conversations about the field. As a venue for knowledge exchange, professional development, and networking, conferences ideally promote positive environments that make scholars from underrepresented groups feel welcome. Yet, negative experiences at conferences are well documented. Codes of conduct have been promoted as tools to reduce harassment and discrimination. This article examines the prevalence and content of codes at US-based political science conferences and workshops. More specifically, we analyze whether and how codes address issues of sexual misconduct and identity-based discrimination. We find that 19% of 177 surveyed conferences have a code of conduct. Conferences that are older and larger are more likely to have codes, as are conferences that are run by organizations with permanent staff and relevant committees. We argue that effective conference codes must contain definitions, reporting channels, and enforcement procedures. Many of the analyzed codes did not explicitly define prohibited behaviors, specify mechanisms to report code violations, or describe consequences for misconduct.

This article presents a short simulation active-learning exercise that can be used in political science undergraduate and postgraduate environmental politics courses to introduce and teach the subject of environmental activism. The exercise, which asks students to role play as determined environmental activists, draws on an analytical framework from Diani and Donati (1999) that provides a typology of nonpartisan political organizations. This “learning through typology” allows students to prioritize critical analysis while engaging with the subject matter in a creative and enjoyable way. The article provides the full process of the exercise, from the grounding in the framework and provision of contextual examples, to the running of the simulation, and finally to the presentation of students’ work and a group debriefing session. This exercise therefore adds to the growing use of simulations and active learning in the increasingly prominent field of environmental politics.

What are the local political, economic, and social conditions of the communities that sent insurrectionists to the US Capitol in support of Donald Trump? Using a new dataset of the home counties of individuals charged for the Capitol insurrection, we tested two prominent theories of electoral populism and support for populist leaders like Donald Trump—demographic change and manufacturing decline—and whether they also explain violent populism. We also examined the effects of local political conditions. We find that white population decline is a stronger predictor of violent populism and that counties that voted for Trump were less likely to fight for Trump. The effect of white population decline is even greater in counties whose US House Representative rejected the 2020 election results. These findings suggest scholars should resist assuming violent populism is merely an extension of electoral populism, and solutions to one will not necessarily remedy the other.