Six Words You Never Knew Had Something to Do with Pigs and Other Fascinating Facts about the Language from Canada's Word Lady (review)
Bibliographic record
Abstract
Reviewed by: Six Words You Never Knew Had Something to Do with Pigs and Other Fascinating Facts about the Language from Canada's Word Lady J.K. Chambers Katherine Barber . Six Words You Never Knew Had Something to Do with Pigs and Other Fascinating Facts about the Language from Canada's Word Lady. Oxford University Press. xvi, 224. $24.95 No language has travelled as far as English or served as the prime medium of communication for so many cultures. And no language has [End Page 175] been so absorbent, soaking up words and phrases from every language it touched. By the time English was first written down, around 700, it already had lots of borrowed words from Norse, Latin, and Celtic. Now, thirteen centuries later, half its vocabulary comes from French and the other half comes from Arabic, German, Greek, Hindi, Italian, Scottish, Spanish, and two dozen other languages including (lest we forget) Anglo-Saxon, the ur-language. Katherine Barber, editor-in-chief of the Canadian Oxford Dictionary, discovered that she had the knack of charming literate listeners with exotica about English words when she was called upon to promote her dictionary. In radio interviews and book-club talks, she took on the persona of the Word Lady, schoolmarmish in style but witty and surprisingly worldly, and unafraid of going wherever her scholarship takes her. (So, for example, she tells us that before loom became the name of a specific weaving tool it had meant any tool at all, and she adds, 'Amusingly it went through a phase from about 1400 to 1600 when it was used to mean "penis,"' as does 'tool' nowadays.) As the Word Lady, Barber became a regular contributor on CBC-Toronto's early-morning show and an irregular contributor in other media. Six Words You Never Knew Had Something to Do with Pigs gathers together numerous radio pieces into a tidy package suitable for browsing, referencing, or even close reading. Barber organizes it topically by the four seasons with sub-topics ('Easter Bonnet and Nine Others for Spring,' 'Tennis Anyone? and Nine Others for Summer,' 'Back to School and 13 More for Fall,' and 'Happy New Year! with 12 More for Winter'). The titular words that have something to do with pigs come early and more or less run the gamut of etymological incredulity. Porcelain looks as if it has pig in it (or at least pork, porcine) but it was named for a seashell, which in turn had been named for its piggy shape. Screw, as in corkscrew (though the Word Lady cites only carpentry screws) comes from Latin scrofa 'sow,' presumably because a sow's tail is (cork)screw-like. As for soil, porpoise, root, and swain, space does not permit tracing their pig origins in this review. You can look them up, of course, along with no fewer than 370 other words, each one given its due in the unfolding almanac-like themes of this handsome little book. The overriding appeal of the book comes from our sense of wonder at the magnificent patchwork that is the English vocabulary. The Word Lady points out, for instance, that hospital, hospice, hostel, and hotel were borrowed into English at four different historical moments, the first from Latin and the others from French developments of that very same Latin root. Extravagant accommodations like these, registering the names for things that are foreign and then adopting the name with the thing, have been repeated thousands of times in the global expansion of [End Page 176] the language. No other language has travelled as far, and the words we use mark its travels like stamps in a passport. J.K. Chambers Department of Linguistics, University of Toronto Copyright © 2008 University of Toronto Press Incorporated
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".