This study investigates the relationship between automatic information extraction descriptors and manual analyses to describe gender representation disparities in TV and Radio. Automatic descriptors, including speech time, facial categorization and speech transcriptions are compared with channel reports on a vast 32,000-hour corpus of French broadcasts from 2023. Findings reveal systemic gender imbalances, with women underrepresented compared to men across all descriptors. Notably, manual channel reports show higher women's presence than automatic estimates and references to women are lower than their speech time. Descriptors share common dynamics during high and low audiences, war coverage, or private versus public channels. While women are more visible than audible in French TV, this trend is inverted in news with unseen journalists depicting male protagonists. A statistical test shows 3 main effects influencing references to women: program category, channel and speaker gender.

翻译：本研究探讨了自动信息提取描述符与人工分析方法在描述电视与广播中性别呈现差异方面的关联性。研究基于2023年法国广播节目构成的32,000小时大型语料库，将包括发言时长、面部分类和语音转写在内的自动描述符与频道报告进行对比。研究结果揭示了系统性的性别失衡现象：在所有描述符中，女性的呈现比例均低于男性。值得注意的是，人工频道报告显示的女性存在感高于自动估计值，且对女性的提及频率低于其实际发言时长。在高低收视时段、战争报道以及私营与公共频道的对比中，各类描述符呈现出共同的动态规律。虽然法国电视中女性的视觉呈现高于听觉呈现，但在新闻节目中这一趋势发生逆转——未出镜记者描绘的新闻主角多为男性。统计检验显示，对女性提及频率主要受三个因素影响：节目类别、频道属性及发言者性别。