Bibliographic record
Abstract
Elephant Crossing. Houdini Needles. Miniskirt, Tickletoeteaser Tower, and Why Not Mountain. These are just some of the many names of places, rivers, mountains, and lakes that you will come across in the newest edition of British Columbia Place Names. This classic which, in its various editions, has sold over 29,000 copies, covers about 2,500 geographical features, cities, towns, and smaller communities in the province. The book abounds with fascinating historical facts, stories, and remarkable characters involved with the names of towns, cities, rivers, lakes, mountains, and islands. The selection was determined by the geographical importance of the feature as well as story of the naming. In the introduction the authors deal with the stages by which B.C. acquired its place names, the history of research into those names, and the categories into which they fall. The latter range from the honorific and commemorative to the comic and disrespectful. Aboriginal names receive particular attention. The location of each place is clearly indicated and the text is accompanied by detailed maps. Brief biographical accounts of persons with places named after them as well as an abundance of anecdotes make this a fascinating book for browsers and an invaluable resource for historians.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.177 | 0.088 |
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".