Bibliographic record
Abstract
Abstract The paper provides a 50‐year overview of the Canadian Journal of Economics, Canada's leading economics journal. I first discuss the evolution of the journal's editorial structure and publication process. I then construct a database of all the articles published from 1968 to 2017 and perform bibliometric analyses. I find a significant increase in the articles’ length as well as an increase in article features that is consistent with a rise in empirical analysis. I also analyze the articles’ topical coverage and trends in their discussion of approaches, methodologies, topics and techniques (AMTTs). International economics accounts for the largest share of the articles. There is also a large increase in the share of articles discussing applied micro AMTTs after the mid‐1990s. Consistent with previous findings, there is a drop in the share of articles with Canadian content. Furthermore, I document an upward trend in co‐authorship that has also been documented in other economics journals. Finally, I analyze Google Scholar citations and show that the most cited articles were published between the early 1990s and the late 2000s.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.051 | 0.068 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".