WHICH JOURNAL RANKINGS BEST EXPLAIN ACADEMIC SALARIES? EVIDENCE FROM THE UNIVERSITY OF CALIFORNIA
Bibliographic record
Abstract
The ranking of an academic journal is important to authors, universities, journal publishers, and research funders. Rankings are gaining prominence as countries adopt regular research assessment exercises that especially reward publication in high‐impact journals. Yet even within a rankings‐oriented discipline like economics there is no agreement on how aggressively lower‐ranked journals are down‐weighted and in how wide is the universe of journals considered. Moreover, since it is typically less costly for authors to cite superfluous references, whether of their own volition or prompted by editors, than it is to ignore relevant ones, rankings based on citations may be easily manipulated. In contrast, when the merits of publication in one journal or another are debated during hiring, promotion, and salary decisions, the evaluators are choosing over actions with costly consequences. We therefore look to the academic labor market, using data on economists in the University of California system to relate their lifetime publications in 700 different academic journals to salary. We test amongst various sets of journal rankings, and publication discount rates, to see which are most congruent with the returns implied by the academic labor market. (JEL A14, I23, J44)
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".