Women Who Break the Glass Ceiling Get a “Paper Cut”: Gender, Fame, and Media Sentiment
Bibliographic record
Abstract
Abstract Past quantitative studies have shown that most media coverage is of men. Here we ask if the scarce coverage that women get is qualitatively different from that of men. We use computer-coded sentiment scores for 14 million person names covered in 1,323 newspapers to investigate the three-way relationship between gender, fame, and sentiment. Additional large-scale data on occupational categories allow us to compare women and men within the same profession and rank. We propose that as women’s fame increases their media coverage becomes negative more quickly when compared to men (a “paper cut”), because their violation of gender hierarchies and social expectations about typical feminine behavior evokes disproportionate scrutiny. We find that while overall media coverage is much more positive for women than for men, this difference disappears and even reverses at higher levels of fame. In encyclopedic sentiment data we find no biographic basis for women’s disproportionate decline in media coverage sentiment at high fame, consistent with the conjectured double standard in media discourse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".