Mathematics & Science Education and Income: An Empirical Study in Japan
Bibliographic record
Abstract
Abstract: Since the second half of the 1990s, the decline in academic standards in mathematics and science among undergraduate students in Japan has been noted. Despite this, problems in science education have become increasingly severe, and their impact is having a mounting effect on Japan's economy. This paper studies the return to a university education in Japan by taking into account the relative ranking of the universities. We present an empirical analysis of how annual income differs depending on whether a major is natural science or humanities. We have found that science graduates have a higher average income than humanities graduates indicates that the added value they are producing is higher than that of humanities graduates. Of particular interest is the fact that a comparison of humanities graduates of A rank universities who did not sit admission examinations in mathematics with science graduates of B rank university showed that it was the science graduates who recorded higher average income at every age grade. The above comparison also reveals that even those humanities graduates of A rank universities who did sit admission examinations in mathematics are out-earned by science graduates of B rank universities in the under 30 and 55 and over age groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".