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Record W3162162969 · doi:10.31234/osf.io/d4k6r

A quantitative evaluation of gender asymmetry in euphemism

2021· preprint· en· W3162162969 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEuphemismFormalityTabooPsychologyPanoramaLinguisticsSocial psychologySociologyComputer scienceArtificial intelligenceAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.262
GPT teacher head0.485
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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