Bibliographic record
Abstract
He has been the editor of the International Journal of Political Economy for nearly 20 years.In 2021, he was awarded the John Kenneth Galbraith Prize in Economics, an honour bestowed every two years by the Progressive Economics Forum, a Canadian organization that participates in the annual meetings of the Canadian Economics Association.His areas of specialization are macroeconomic theory, monetary economics, labour economics, history of economic thought and Canadian economic history. Mario, how did you become an economist?When I finished high school in Montreal, I really had no idea what I was going to study and economics was not on my radar.I had to take a bunch of courses that were compulsory for the programme of arts and sciences at McGill University where I had chosen to pursue my undergraduate studies.The norm was to take five two-semester courses.So, I registered in courses such as English literature, political science, philosophy, history, and also a two-semester differential and integral calculus course.But friends of mine were taking six courses and one of them decided to go into an economics course.I went for an extra course as well, just to inflict pain on myself I suppose; this was an economics course.However, it was not the sort of economics course that students take in first year nowadays, you know, the introductory course that is so dry and dreary, dealing only with supply and demand, or marginal utility and all that.It was a first-year economic history course, broad-based, connected with what we had been doing earlier in high school, something with which we could identify, trying to understand the economic history of the world or of the country, the Great Depression, and so on.It turned out to be the course that I enjoyed the most, not only because of the material but also because of the professor.So, who was the professor?It was John F. Henry, who unfortunately passed away barely a year ago.Many people know him because for a very long time he was teaching at California State University in Sacramento immediately after leaving McGill, but he then went to the University of Missouri in Kansas City.This was after his retirement in California, so he spent close to ten more years teaching at UMKC until his retirement from the latter in 2014.He was just a terrific guy.He was humorous, he was very kind, and he was just great and inspiring to listen to.What year was this first-year course in economics?It was a two-semester course that I took during the 1969-1970 academic year.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".