Allan Tupper: No Place to Learn Why Universities Aren't Working: An Interview with Allan Tupper
Bibliographic record
Abstract
Allan Tupper is Associate Vice President (Government Relations) and Professor of Political Science at the University of British Columbia. A native of Ottawa, Dr. Tupper is a graduate of Carleton University (BA, DPA, MA) and Queen's University where he received his PhD in Political Studies in 1977. For more than 20 years, he was Professor of Political Science at the University of Alberta. He served as Chair of the Department of Political Science, Associate Dean of Arts and Associate Vice President (Government Relations). He was also Vice President (Academic) at Acadia University. His major teaching and research interests are Canadian politics, western Canadian politics, public policy and public administration. He has published extensively on these topics and has authored or edited six books and many articles and chapters. Dr. Tupper is Editor in Chief of Canadian Public Administration, the internationally-known journal of the Institute of Public Administration of Canada. He chairs the Centre for Constitutional Studies, an established research institute for the interdisciplinary study of constitutional and human rights issues in Canada and abroad. Dr. Tupper is actively involved in community outreach, public speaking and media relations. He is a frequent commentator on regional and national media. Dr. Tupper has been an instructor at the Banff School of Advanced Management and the Senior Executive Development Program of the Government of Alberta.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.045 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".