Golly, Gosh, and Oh My God! What North American Dialects can Tell Us about Swear Words
Bibliographic record
Abstract
This article presents a synchronic quantitative study of nearly 3,000 words and expressions referring to ‘God’ in Ontario, Canada. The results expose a number of striking social and linguistic patterns. Using apparent time as a proxy for historical change, we discover that G-words have undergone a remarkable shift across the twentieth century. Euphemisms, such as Golly and Gosh, are quickly moving out of favor, and the expressions with God, particularly oh my God, have usurped all other forms. Moreover, there are clear regional differences. Rural communities retain old-fashioned euphemisms, and there are notable social contrasts to their use: females favor gosh while males favor gee(z), and both are favored by less-educated speakers. Variants with God are not only predominant in the urban center, Toronto, they reflect known societal change in North America as a whole. Younger speakers in every community shift toward noneuphemistic practice beginning in the 1930s, increasing after World War II and accelerating again among people born after 1960. However, this shift is not simple lexical replacement. Where once individuals used God in collocations such as Praise God or Thank God, people born in the early 1960s onward are using God in one collocation in particular: oh my God (n = 611). A fascinating correlate is that, as with many changes, this is being led by higher-educated women who have white-collar jobs. These findings reflect not only a greater acceptance of the word God in contemporary society, but also egalitarian diffusion across the population, both geographically and socially.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".