Bibliographic record
Abstract
University programs differ in the subsequent earnings processes of their enrollees, including many features that students might care about to differing degrees such as the level of average earnings, earnings growth, and volatility. Do the earnings features of a university program’s enrollees reflect the causal effect of enrolling in that program or the self-selection of students into that program? Would students experience a different earnings process if they enrolled in a different program of study? To estimate the causal impact of enrolling in a program of study on the enrollees’ future earnings process, we exploit a discontinuity built into the Danish national university admissions system, which provides quasi-random assignment of similar applicants to different programs. We leverage the rich cross-program variation in the enrollees’ future earnings processes to measure the impact of entering a program whose enrollees experience high earnings levels, growth, and volatility on their own subsequent earnings level, growth, and volatility. We find that a student’s subsequent earnings levels and volatility – but not their earnings growth – are caused by entering programs of study whose enrollees have those features.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".