三輪 洋文

Understanding the ideological positions of political parties is crucial for empirically testing various theories in political science. In Japan, some studies have assessed each party’s policy outlook through electoral platforms, candidate surveys, or expert surveys, with most studies focusing on the past thirty years. However, limited efforts have comprehensively and longitudinally quantified the conflicts between political parties. Although previous studies have used election-related data to reveal interparty conflicts during elections, interparty conflicts between elections remain unclear. This study determines the stance of Japanese political parties during parliamentary debates. We collected text data from committee speeches from the Diet Proceedings Database from 1959 to 2019 and conducted a quantitative text analysis. By applying the Wordshoal method, we first estimated political party positions on specific policy topics in the House of Representatives and the House of Councillors. Then, we aggregated these policy-specific positions yearly via dynamic factor analysis. Consequently, we succeeded in discerning the axis of conflict in Japanese parliamentary debates and evaluated the longitudinal one-dimensional ideal points for political parties. Additionally, we visualized the changes in each party’s ideological position, identified controversial topics, and explored how these topics changed over time, providing new insights into the study of Japanese politics.

Voter preferences for a politician’s gender have been considered a potential factor of women’s underrepresentation in politics. Although recent studies demonstrate that voters do not generally dislike women candidates in elections, they may still doubt women’s competence and view persuasive behavior as counter-stereotypical for women. This may lead to the prediction that female politicians are less effective in persuading voters through their policy statements compared to men. However, evidence on this issue is scarce and mixed, with existing studies often having notable weaknesses in their experimental design. To address these problems, we carefully designed and conducted two preregistered survey experiments in the U.S. and Japan, where electoral rules are candidate-centered, and women’s underrepresentation is severe compared to other developed countries. We asked respondents how much they agreed with the policy statement of a fictitious politician whose gender was randomized in a mocked campaign website or a municipal council newsletter. We also varied the statement’s issue area and ideological position to examine whether persuasion is more effective when it aligns with gender stereotypes. The results showed no significant difference in policy persuasiveness between women and men politicians, and post hoc equivalence tests suggest the nonexistence of even minimal effects in both countries. Moreover, we found partial evidence that women politicians had an advantage when they claimed gender-stereotype statements in Japan, while the opposite was true in the U.S.

Detecting and addressing discrimination is one of the most crucial ways for ethnic and sexual minorities and women to fully integrate into the society. However, humans often fail to correctly judge what constitutes discrimination. The recent rapid development of artificial intelligence (AI) based on large language models (LLMs) has shown promise in assisting with this task, although its performance is understudied. This study investigates how humans and LLM-based AI, such as OpenAI’s ChatGPT, detect the concept of ‘discrimination’ and how their representation differs. Specifically, surveys were conducted asking humans (Japanese respondents) and ChatGPT (GPT-4) to evaluate the degree of discrimination in hypothetical unequal treatment scenarios presented to them. The scenarios varied in terms of targets, including their ethnicity, gender, and sexuality, and types of discrimination, such as those based on tastes, stereotypes, and statistics. The results show that ChatGPT generally classifies the scenarios as more discriminatory than humans do. However, ChatGPT also shares a tendency with humans to be more tolerant of unequal treatment based on ethnicity and gender compared to sexuality, and it is less likely to detect statistical discrimination than taste- or stereotype-based discrimination. Although LLM-based AI presents a potential tool for addressing discrimination and can offer temporary solutions, it may not fully capture all types of discrimination.

In coalition governments, while parties need to compromise with their partners to govern together, they also need to differentiate from the partners to please their supporters. How do coalition parties reconcile compromises with differentiation? We argue that coalition parties, especially junior partners, adjust their strategies to compromise or differentiate themselves from their partners depending on the level of public support for their partners. Parties tend to differentiate from partners when partners receive lower public support, and compromise with partners when they enjoy higher public support. To test this argument, we analyzed parliamentary speeches by legislators from a junior coalition partner in Japan. We applied a dynamic linear model to the results of machine learning text classification of legislators’ speeches. Our results show that as the senior party’s public support rate falls, the speech styles of legislators from the junior partner increasingly diverge from those of the senior partner’s legislators.

Incumbents often serve as critical gatekeepers in the recruitment of new candidates and may even designate their successors upon retirement. Some existing research indicates that the gender of gatekeepers is likely to affect the recruitment of female candidates, a dynamic of particular concern in countries like Japan, where political offices are predominantly held by men. However, it remains unclear whether the underrepresentation of women stems from male incumbents actively discriminating against female candidates during the recruitment process. Through a survey experiment involving over 7000 elected local politicians in Japan, we examine gender biases in the successor selection process and attitudes toward female candidacy. Contrary to our expectations, the results reveal that local politicians, irrespective of their own gender, are more inclined to nominate women over men as their successors. They also believe that these female candidates would receive support from their local constituencies. These findings suggest that the selection practices of incumbents may not significantly contribute to the underrepresentation of women in politics.