Predicting the impact of <i>American Economic Review</i> articles by author characteristics
Bibliographic record
Abstract
Abstract Authors who publish in American Economic Review (AER) have career paths confined to a few prestigious institutions, and they mostly have exceptional past publication performance. In this paper, I show that authors who are educated and work in the top 10 institutions and have better past publication performance receive more citations for their current AER publications. Authors who have published in the top economic theory journals receive fewer citations even after controlling for the subfield of their AER article. The gender of the authors, years of post-PhD experience, and the location of the affiliated institution do not have any significant effect on the citation performance. An opportunistic editor can exploit the factors that are related to citation performance to substantially improve the citation performance of the journal. Such opportunistic behavior increases the overrepresentation of authors with certain characteristics. For example, an opportunistic editor who uses the predicted citation performance of articles to select a quarter of the articles increases the ratio of authors who works at the top 10 institutions from 30.8% to 52.0%.
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 | 0.009 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".