Bibliographic record
Abstract
My title, So Vast and Various, comes from George Parkin's 1895 introduction to his book on Canada: "It need scarcely be added that in regions so vast and various Nature is often seen in her most splendid and picturesque aspects" (The Great Dominion, 7).Every writer on the geography of Canada has to face up to its great regions, with their distinctive scenery, people, cultures, ways of making a living, communications, and settlements.This book allows us to see how seven authors, writing over a span of almost a century and a half, from 1831 to 1977, describe Canada's major regions and in so doing help elucidate the country.Both Canada and the ways of describing it changed over that long period, and through the authors' perceptions of regions at different times we get a deeper understanding of the regional nature of Canada and in turn of the country as a whole.This project has its origins in an article I published in 1999 in The Canadian Geographer on the regional writing of five perceptive observers of Canada: Joseph Bouchette, a surveyor from Lower Canada who wrote on British North America in the early nineteenth century; George Parkin, an educator and journalist from New Brunswick who described the country in the late nineteenth century; J. D. Rogers, a British barrister, scholar and onetime fellow of University College, Oxford, and Harold Innis, an economic historian from Ontario, who both provided regional interpretations of Canada in the first quarter of the twentieth century; and Bruce Hutchison, a journalist from British Columbia who viewed Canada's regions through encounters with its men and women in the mid-twentieth century.All brought out the inherent regional differences within Canada in the time period in which they were writing.This article caught the eye of a member of the editorial board of
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.416 | 0.186 |
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".