Bibliographic record
Abstract
This wonderful collection of 76 essays explores the fascinating origin and meaning of the names of some of the towns, villages, cities, islands, mountains, and rivers that make up one of the world's largest countries. This new edition includes fifteen more essays, and updates the previous essays to include changes, corrections and new names to the year 2000. Discover how some of Canada's most unusual place names came to be; unearth the Aboriginal roots of names such as Miramichi, Klondike, Iqaluit, Toronto, and Ottawa; learn the origin of such playful and mellifluous names as Medicine Hat, Twillingate, Flin Flon, Cupids, or Saint-Louis-du-Ha! Ha! From Bonavista and Port au Choix in the east, to Malaspina Strait and Port Alberni in the west, this book also reveals the rich Portuguese, Spanish, and Basque contributions to Canada's toponymic heritage. Naming Canada tells us about place names that became undesirable and had to be changed for reasons of perceived political impropriety. The former Stalin Township, for example, was renamed after Rick Hansen, the renowned Man in Motion, who promoted research in spinal cord injuries. The book also discusses Canadian names that have been exported abroad, such as Quebec in England and Toronto in Australia. One new essay explores the nicknames used for Canadian places, and focuses on Hogtown as an alternative for Toronto. This collection is the best single source, in an engaging essay format, on the origin and meaning of hundreds of Canadian place names. Alan Rayburn has had over 35 years' experience in researching Canada's toponymic roots and in writing about the authentic backgrounds behind thousands of names, from Toronto in the south to Tukyoyaktuk in the north, and from Labrador in the east to Juan de Fuca Strait in the west.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.098 | 0.023 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".