Bibliographic record
Abstract
<title>Abstract</title> This paper utilizes data from the official American Economic Association (AEA) website, previously unexplored in literature, to investigate the choice of economic topics across all AEA journals. Employing bibliometric analysis and Journal of Economic Literature (JEL) Codes, we discern that micro-related papers are more prominently featured in AEA journals. Furthermore, through an examination of selected topic trends over time, we observe a sustained increase in the proportion of papers related to microeconomics and mathematical and quantitative methods in the American Economic Review (AER). Overall, our findings suggest that researchers should take into account the topic preferences of specific journals when selecting journals for paper submission, even those purportedly accepting papers on a wide range of economic topics. Additionally, for success in academia, junior researchers should start considering aligning their research interests with prevailing hot topics in academic journals. <bold>JEL Codes</bold>: A11, A14
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".