Bibliographic record
Abstract
Was born in 1922 in Trieste, Italy, and died on September 30, 2019 in Vancouver, Canada. He grew up in Vienna, graduating with a Dr. rer.pol. in 1945, Degree of Doctor of Economic Sciences, Hochschule fur Welthandel, nowadays Wirtschaftsuniversitat Wien, Economic University of Vienna. He had the following academic positions: fellow of the Austrian Institute of Economic Research, Vienna (1945-47); lecturer at the Rosenberg College (St. Gallen, 1947-52); then he emigrated to Canadà, where he became professor of commerce and economics and Department Head of Commerce at Mt. Allison University (Sackville, N.B. 1953-59), after working for a year in an insurance company, Actuarial and Auditing Department, in Montreal; from 1959 to 1967 he served as a tenured associate professor, University of California, Berkeley, School of Business Administration , following one year in a visiting position; in 1966-67 he simultaneously held a chair in economics at the Ruhr Universitat, Bochum, Germany; the final position was at University of British Columbia, Arthur Andersen chair (Vancouver, 1967-87; since 1987 Prof. Emeritus); professor, Technische Universitat (Vienna, 1976-78—simultaneously with his position at UBC); he held also various visiting professorships at universities in Austria, Germany, Italy, Japan, New Zealand, Spain and Switzerland.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.095 | 0.077 |
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".