Sunkyoung Park

Abstract Large language models are increasingly applied to political discourse, but their ability to detect culturally grounded gender sensitivity remains underexplored. We introduce KOGENT, a benchmark dataset of 1,222 transcripts from the Korean National Assembly, annotated for gender sensitivity across 6,024 utterances. Each utterance is labeled as high or low in gender sensitivity, based on contextual indicators of bias, discrimination, or inclusion, and tagged for the target group.

Supporting Information Filename Description ajps12716-sup-0001-Appendix.pdf9.8 MB Appendix A: Additional background information on elections Appendix B: Descriptive statistics, tables showing full results, and robustness checks Appendix C: Description and additional analyses of survey data for mechanism analysis Appendix D: Sample raw data showing the NVM leaders and assigned local officials References . 2019.

Recently, the authors identified an error that occurred during the collection and compilation of our dependent variables measuring long-term effects, specifically the election outcomes. This impacted the results reported in Table 2 and Table 3, as well as the illustration in Figure 4.