  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.
