Economics Research in Canada: A Long-Run Assessment of Journal Publications
Bibliographic record
Abstract
We examine the publications of authors affiliated with an economics research institution in Canada in (i) the Top-10 journals in economics according to journals' impact factors, and (ii) the Canadian Journal of Economics. We consider all publications in the even years from 1980 to 2000. Canadian economists contributed about 5% of publications in the Top-10 journals and about 55% of publications in the Canadian Journal of Economics over this period. We identify the most active research centres and identify trends in their relative outputs over time. Those research centres successful in publishing in the Top-10 journals are found to also dominate the Canadian Journal of Economics. Additionally, we check the robustness of our findings with respect to journal selection, and we present data on authors' Ph.D.-origin, thereby indicating output and its concentration in graduate education.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.053 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".