Bibliographic record
Abstract
Why are some words rude and others aren't? Why can launching into expletives be so shocking - and sometimes so amusing? In this hilarious extract from his bestselling The Stuff of Thought Steven Pinker takes us on a fascinating journey through the world of profanities, to show us why we swear, how taboos change and how we use obscenities in different ways. Why do so many swear words involve sex, bodily functions and religion? What are the biological roots of swearing? Why would a democracy deter the use of words for two activities - sex and excretion - that harm no one and are inescapable parts of the human condition?Taboo language enters into a startling array of human concerns from capital crimes in the Bible to the future of electronic media. You'll discover that in Quebecois French the expression 'Tabernacle' is outrageous, that 'scumbag' has a very unsavoury origin and that in a certain Aboriginal language every word is filthy when spoken in front of your mother-in-law. Covering everything from free speech to Tourette's, from pottymouthed celebrities to poetry, this book reveals what swearing tells us about how our minds work. (It's also a bloody good read).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.027 |
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".