Ranking of Research Output of Agricultural Economics Departments in Canada and Selected U.S. Universities
Bibliographic record
Abstract
In this paper, we rank agricultural economics departments in Canada on the basis of research output as measured by citations, publications, and publications weighted by journal impact factors. The data we employ are from the ISI Web of Science and cover the period 2000–07. In this ranking we include three departments from the United States to assess the performance of Canadian institutions relative to U.S. departments. We also investigate how publication output is affected by academic rank. Several Canadian departments compare favorably to some of the U.S. departments. Dans cet article, nous classant les départements d’économie agricole au Canada sur la base du nombre de citations, de publications et de publications pondéré par les coefficients d’impact des revues scientifiques. Les données sont disponibles à partir de ISI Web of Science®et couvrent la période 2000–07. Dans ce classement, nous incluant trois départements des Etats‐Unis pour évaluer la performance des départements Canadiens par rapport à ceux‐ci. Nous examinons aussi l’effet du rang académique sur les publications. Plusieurs départements Canadiens sont très comparables à quelques départements des Etats‐Unis.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.043 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".