Bibliographic record
Abstract
Kandy, Ceylon. 1About 1945, when I was 12 years old, the sweet sounds of these two names were observed in an atlas. I dreamed that I might visit them some day. That dream became reality in June, 1959, when I ate a Chinese dinner and had a cup of Ceylon tea in Kandy. It was the embryonic touchstone of a most satisfying career in the study of toponymy. Throughout my primary and secondary education in Orangeville, Ontario, I developed a deep interest in maps, cultural geography, and Canadian and American history. In 1952, I enrolled in honors history studies at Waterloo College, a satellite campus of the University of Western Ontario, where I was awarded a bachelor's degree in 1956, after having spent my third year at the University of British Columbia. At both Waterloo and UBC, I studied with several erudite professors of history and geography, who whetted my desire to pursue studies of the creation and development of settlements. That desire led me to enroll in 1956 in a master's program in geography at the University of Kentucky, where I received an MA a year later, having written a thesis on the settlement geography of my hometown. My thesis advisor at the University of Kentucky was Thomas Field. A native of North Carolina, Tom Field lectured on cultural geography, with special attention on the South Pacific. In one of the rooms in the Geography Department, there was an extensive card collection on the place names of Kentucky, which he had assembled over several years. One day, the usually mild and meek Tom· Field vented his anger over the naming of Cumberland Gap and Cumberland River. They had been named by explorer Dr. Thomas Walker in the mid-1700s after George II's third son William Augustus (1721-65), the ruthless Billy the Butcher at the Battle of Culloden in 1746. The seed was planted in me to pursue the study of place names, if not for a career, at least as an avocation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.111 | 0.039 |
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".