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Record W3149232676 · doi:10.6000/1929-7092.2013.02.1

Mathematics & Science Education and Income: An Empirical Study in Japan

2013· article· en· W3149232676 on OpenAlexvenueno aff
Junichi Hirata, Kazuo Nishimura, Junko Urasaka, Tadashi Yagi

Bibliographic record

VenueJournal of Reviews on Global Economics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMathematics educationMathematicsDemographic economics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.419
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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