Sophie Borwein’s research while affiliated with University of British Columbia and other places

In this research note, we propose a new typology of geographic places for use in advancing Canadian politics research on the political effects of place. We use hierarchical cluster analysis (HCA) to inductively derive place types from Aggregate Dissemination Areas (ADAs), a fine-grained unit of geography covering all of Canada, newly available from Statistics Canada. Using this approach, we uncover seven distinct place types, adding nuance to earlier more aggregated approaches focused on urban-rural (or urban-suburban-rural) distinctions. We then demonstrate our typology's utility by showing how it provides new insight into the relationship between place and politics, using the example of the 2021 Canadian federal election. We conclude with a discussion of how our approach may be used to further the study of Canadian political geography, and we make available all files necessary for researchers to merge our place types with survey data for this purpose.

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The potential for artificial intelligence to disrupt life and work has prompted debates on its regulation. This paper examines the gender gap in attitudes toward AI, focusing on how differences in risk-taking influence support for AI adoption and regulation. Using survey data from 3,000 respondents in Canada and the United States, we find that women are more skeptical of AI’s economic benefits and more likely to emphasize its risks, such as job displacement. This gap appears to be linked to women’s higher general risk aversion and greater exposure to AI-related risks. Experimental evidence shows that as AI’s benefits become more uncertain, women’s support for its adoption drops more sharply than men’s, while their support for government intervention increases. Given AI’s potential to exacerbate gender inequalities, policies that overlook women’s perspectives risk perpetuating workplace and societal disparities.

Artificial intelligence is poised to reconfigure the economy and politics. Although new technologies often produce net economic gains, their costs and benefits are unequally distributed, making them susceptible to politicization. We argue that whether and how AI becomes mobilized for partisan gain will depend on the public’s causal beliefs about the winners and losers of AI. We capture these causal beliefs using a novel survey instrument fielded with 6,056 Americans and Canadians. Through latent class analysis, we show that a significant portion of the public theorizes AI to be a threat—harming consumers and replacing rather than complementing workers’ skills. These causal theories are already aligned with political preferences, predicting support for policies that delay job loss over those that help workers adapt, and polarizing voters along existing partisan lines. We conclude that fissures in the public’s attitudes toward AI already exist and can be easily channeled toward politics.

Urban–rural divides are large and growing in many national elections, but the sources of this widening divide are not well understood. Recent research has pointed to policy disagreement as one possible mechanism for this growing divide; if urban and rural residents hold increasingly dissimilar policy preferences, this disagreement could produce ever‐widening urban–rural electoral divides. We investigate this possibility by creating a synthesized dataset of nearly 1000 policy issue questions across 10 distinct Canadian national election studies conducted between 1993 and 2021 ( N = 5.3 million), combined with a measure of the urban or rural character of every federal electoral district. This dataset allows us to measure urban–rural policy disagreement across a much larger range of policy issues and over a much longer time period than has previously been possible. We find strong evidence of urban–rural policy disagreement across a range of issues, and especially in areas of cultural policy, including questions relating to gun control, immigration and Indigenous affairs. We further find strong support for the ‘progressive cities’ hypothesis; in nearly all policy domains, urban residents support more left‐wing positions on policy issues than rural residents. However, we find no evidence these urban–rural policy divides have grown since the 1990s. Urban–rural policy disagreement, while large and meaningful, cannot explain the ever‐widening urban–rural political divide.

Advances in artificial intelligence (AI) are reshaping labor markets and sparking political debates. Like economic globalization, AI developments promise benefits, including job creation and lower prices, but also costs such as job displacement, raising crucial questions about public perceptions. Will AI, like globalization, challenge existing paradigms and trigger a backlash? Leveraging a conjoint experiment with 6,000 respondents from the United States and Canada, we examine public opinion toward offshoring and generative AI, focusing on the multidimensional trade‐offs between job and price changes. Across all scenarios, respondents are equally or more sensitive to price changes than employment shifts. AI is favored over offshoring, especially among Democrats, highlighting an emerging partisan divide in the United States. Republicans and Canadians show more varied support, indicating AI is not immune to opposition. By focusing on the microfoundations of opinion formation, we identify scenarios that may trigger or temper protectionist stances.

The potential for artificial intelligence to disrupt life and work has prompted debates on its regulation. This paper examines the gender gap in attitudes toward AI, focusing on how differences in risk-taking influence support for AI adoption and regulation. Using survey data from 3,000 respondents in Canada and the United States, we find that women are more skeptical of AI’s economic benefits and more likely to emphasize its risks, such as job displacement. This gap appears to be linked to women’s higher general risk aversion and greater exposure to AI-related risks. Experimental evidence shows that as AI’s benefits become more uncertain, women’s support for its adoption drops more sharply than men’s, while their support for government intervention increases. Given AI’s potential to exacerbate gender inequalities, policies that overlook women’s perspectives risk perpetuating workplace and societal disparities.

Rapid advances in artificial intelligence (AI) present substantial economic and social opportunities but also significant risks for different groups in society. This paper examines the gender gap in attitudes toward AI adoption, with a focus on how gender differences in risk orientation and perceptions drive skepticism toward AI’s economic benefits. Using original survey data from approximately 3,000 respon- dents across Canada and the United States, we find that women consistently perceive AI to be riskier than men. We identify two key drivers behind this gender gap: women’s higher general risk aversion and their greater exposure to AI-related risks. To establish a causal relationship between risk and AI attitudes, we further show experimentally that as the perceived benefits of AI become more uncertain, women’s support for companies adopting AI falls more sharply than men’s. Finally, structural topic modeling of open-ended responses confirms that women express greater uncertainty about AI’s benefits and more frequently anticipate little to no benefits. Given AI’s potential to exacerbate existing gender inequalities, our study highlights the critical importance of incorporating women’s perspectives into AI policy-making. Policies that do not address gender-specific risks may not only reinforce existing inequali- ties in employment and income but could also generate political backlash against AI adoption, reshaping political cleavages along gender lines.

Recent political developments in established democracies have renewed attention to the politics of identity. Some commentators have expressed concern that polities are fracturing along increasingly narrow social identity lines, in the process, losing their ability to build solidarity around shared commitments such as redistribution. This article takes stock of the strength of Canadian social identities and their consequences for redistributive preferences. It asks: first, which group memberships form the basis of Canadians’ perceptions of shared identity, and second, do these group memberships shape preferences for redistribution? This study answers these questions using two conjoint experiments that assess respondents’ perceptions of commonality and support for redistributing to hypothetical Canadians who vary on multiple dimensions of identity and need. Findings support that Canadians perceive greater shared identity with some of their groups (their social class) over others (their region or ascriptive identity), but that they overwhelmingly prioritize redistributing toward those who need it over those with whom they share group memberships.

In developing regulatory frameworks to address generative AI’s transformative potential, governments will be guided, in part, by how the public views these technologies. How do people reason about the economic effects of generative AI? We answer this question by examining people’s causal theories of generative AI’s effects on firms, consumers, and workers, using a survey of 6,056 Americans and Canadians. Through latent class analysis, we show that a significant portion of the public views AI as a threat—harming consumers and replacing rather than complementing workers’ skills. We further show that these views are consequential for policy preferences, predicting support for policies that would halt immediate job loss over those that help workers adapt. Finally, we show that voters are already polarized on the basis of their views of technology, particularly in the United States; Americans who see technology as harming consumers and substituting workers’ skills are significantly more likely to vote Republican, while voters who see technology as complementing workers’ skills and producing consumer benefits are significantly more likely to vote Democrat. We conclude that fissures in the public’s attitudes toward AI are emerging and are easily channeled toward politics.