WHICH JOURNAL RANKINGS BEST EXPLAIN ACADEMIC SALARIES? EVIDENCE FROM THE UNIVERSITY OF CALIFORNIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it