Bibliographic record
Abstract
This is the only encyclopedia and social history of swearing and foul language in the English-speaking world. It covers the various social dynamics that generate swearing, foul language, and insults in the entire range of the English language. While the emphasis is on American and British English, the different major global varieties, such as Australian, Canadian, South African, and Caribbean English are also covered. A-Z entries cover the full range of swearing and foul language in English, including fascinating details on the history and origins of each term and the social context in which it found expression. Categories include blasphemy, obscenity, profanity, the categorization of women and races, and modal varieties, such as the ritual insults of Renaissance "flyting" and modern "sounding" or "playing the dozens." Entries cover the historical dimension of the language, from Anglo-Saxon heroic oaths and the surprising power of medieval profanity, to the strict censorship of the Renaissance and the vibrant, modern language of the streets. Social factors, such as stereotyping, xenophobia, and the dynamics of ethnic slurs, as well as age and gender differences in swearing are also addressed, along with the major taboo words and the complex and changing nature of religious, sexual, and racial taboos.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.225 | 0.123 |
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".