‘What the X’ in Anglophone government meetings: Areal distribution, emotionality, and euphemism
Bibliographic record
Abstract
• Offensive expressions vary by region in English-speaking government meetings. • Euphemisms vs. “what the hell” differ in emotional intensity and acceptability. • emotion2vec reveals anger levels in speech across English-speaking countries. This article examines the use of potentially offensive expressions, specifically “what the hell” and its euphemistic variants, in local government meetings across English-speaking countries. Two primary research questions are addressed: first, are there noticeable differences in the frequency of these expressions between countries and within regions? And second, how do euphemistic alternatives compare to “what the hell” in terms of emotional intensity and valence, both across and within national varieties? The study draws on data from three large, recent corpora of geolocated automatic speech recognition (ASR) transcripts and the corresponding underlying audio to explore the geographic distribution and emotional nuances of these expressions in various English-speaking countries, including the US, Canada, the UK, Ireland, Australia, and New Zealand. To assess the emotionality of expressions, specifically anger, the speech emotion recognition model emotion2vec is employed. The findings provide insight into how the acceptability and emotional weight of “what the hell” and variants differ across regions. Additionally, the study demonstrates the potential of vector-based representations of speech in multimodal corpus analysis, while empirically validating theoretical claims in semantics related to pejoration and euphemism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 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".