Bibliographic record
Abstract
This talk draws heavily on my joint work on endogenous growth theory with Philippe Aghion, and also on many ideas that I learned from fellow members of the Economic Growth and Policy Program of the Canadian Institute for Advanced Research during my association with that program from 1994 to 2003, especially Dick Lipsey, Paul Romer and Nathan Rosenberg, none of whom bears any responsibility for the contents. I would also like to thank the members of the Department of Economics at the University of Saskatchewan for their hospitality at the time of the lecture and for their extreme patience in waiting for It is an honour to be giving the 16 th lecture in this series honouring the memory of Mabel Timlin, one of the pioneers of modern Canadian economics. My talk will concern an area of economics to which I don’t think Timlin ever contributed directly, namely the theory of economic growth, but my main message concerns something to which she contributed a lot in many ways, namely
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.767 | 0.580 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".