Public versus Private: Economic Inequality Within Educational Access
Bibliographic record
Abstract
Since the late 1970’s, economic inequality in modern postindustrial countries has been on the rise. This occurred after its steady decline following the Great Depression. Explanations for this phenomenon have identified multiple factors facilitating this reversal of trends. Within this framework, economic transitions of the last few decades have played a key role. Previously Fordist-organized economies of the advanced capitalist world – most of Europe, Australia, Canada, New Zealand, and the United States – deindustrialized into knowledge-based economies. In conjunction, and as a result, education has become ever more important for labor market integration. Therefore, education, or lack thereof, is a central mechanism underpinning levels of inequality. But what of different methods of educational access – namely, public and private education? This work examines what influence, if any, either method has on economic inequality. Using cross-national data between 1995 and 2016 within 19 postindustrial countries, this paper finds that heavier reliance on private education results in greater levels of market income inequality and wage dispersion. Conversely, higher levels of public education spending are associated with lower levels of both measures. Additionally, these findings show a mixed association between tertiary educational attainment and wage dispersion. This work sheds additional light on contemporary determinants of rising economic inequality and further advances the importance of social investment and a human capital framework. In particular, this work stresses the relevance of not just any source of investment, but in public social investment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| 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".