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Record W102067309

Trade, Income Inequality and Investment in Education

2008· article· en· W102067309 on OpenAlexaff
Alena Kimakova

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsYork University
Fundersnot available
KeywordsOpenness to experienceEconomicsSubsidyEconomic inequalityInvestment (military)Labour economicsEducational attainmentIncome distributionWageInequalityHigher educationForeign direct investmentDemographic economicsEconomic growthMacroeconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The strong income distributional effects of international trade and the widening wage differential between skilled and unskilled labour in developed countries have been well documented. Consequently, researchers and some policy-makers have argued that increasing sectoral labour mobility and the educational attainment of the population should be a policy priority. This paper looks at the empirical relationships between income inequality, openness, educational attainment and the regulatory framework of tertiary education in OECD countries. Specifically, the objective of the paper is to empirically assess the impact of public versus private investment in university education, tuition fees, educational subsidies and access to student loans on income inequality in a cross-sectional analysis of OECD countries while accounting for their degree of openness to international trade.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.306
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes1
Has abstractyes

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