A quantitative evaluation of gender asymmetry in euphemism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gender has long been discussed as a possible factor in how people speak differently. One gender-based difference asserted by scholars is that women use euphemisms more than men. Although there have been a number of studies investigating gender differences in language, the claim about euphemism usage has not been tested comprehensively. Using four large text corpora of English, we evaluate the claim that women use euphemisms more than men do through a quantitative analysis. We assembled a list of 106 euphemism-taboo pairs to analyze their relative use by each gender in the corpora. Our results do not show that women use euphemisms with a higher proportion than men. We repeated the analysis using different, more selective subsets of the euphemism-taboo pairs list and found that our result was robust. Our study indicates that in a broad range of settings involving both spoken and written speech, and with varying degrees of formality, women do not use euphemisms more than men.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it