M. C. Urquhart, interviewed by R. Marvin McInnis
Bibliographic record
Abstract
Malcolm Charles (Mac) Urquhart was the Sir John A. Macdonald Professor of Political and Economic Science at Queen’s University, Kingston, Ontario. He was born in Alberta in 1913 and died in Kingston in 2002. He was educated at the University of Alberta, completing a B.A. degree in honours economics in 1940, after some years as a part-time and correspondence student while teaching at a rural school. From this he proceeded to the graduate program at the University of Chicago. After completing his coursework at Chicago he taught for a year at MIT, but World War II interrupted his academic life; he was summoned to Ottawa in 1943 to work in the Canadian Ministries of Finance and of Reconstruction. He moved to Kingston in 1945 to join the faculty at Queen’s. M MI, a long-time colleague at Queen’s, conducted the interview in two sessions a couple of weeks apart in February 1995. The site was Urquhart’s office, which the University kindly permitted him to use for more than two decades of post-retirement research. McInnis adds: Mac Urquhart’s academic career at Queen’s was punctuated only by a development mission to Pakistan and sabbaticals at Berkeley and the LSE. As Head of Department in the 1960s he was instrumental in establishing a highly regarded Ph.D. program and in building up a modern faculty in Economics. He was elected Fellow of the Royal Society of Canada in 1966, was President of the Canadian Economics Association in 1969, and was awarded the Royal Society’s Innis-Gérin Medal in 1983. His two monumental contributions to Canadian economic history are the Historical Statistics of Canada (1965) and Gross National Product Canada, 1870-1926 (1993). Although both were collaborative efforts, they were strongly shaped by his sure hand as manager and editor. He also published an article on “Capital Accumulation, Technological Change, and Economic Growth” in 1959 in the old Canadian Journal of Economics and Political Science, pursuing the line of thought widely associated with Solow, Swan and Abramovitz. The article was part of what would have been Mac’s doctoral dissertation. The ideas were formulated in the late 1940s at the University of Chicago and presented at a workshop there in 1953, so he was in on the ground floor of modern growth accounting. Mac Urquhart exhibited a rate of scholarly output that seemed to accelerate with age and continued to show up at his office until 2001.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".