Financial Inequity in Basic Education in Selected OECD Countries
Bibliographic record
Abstract
This is a study of financial disparities in primary and secondary education in OECD countries that have a relatively large population and a school finance system with decentralized features. These countries include the United States, Britain, Australia, Spain, Canada, and Japan. There are two major research questions: What are the trends in disparities in per-student education spending during the 1990s and early 2000s period? What government policies or factors may explain these trends? Common statistics such as the coefficient of variation, the restricted range, the federal range ratio, and Gini coefficient are used to measure disparity in per-student spending. Sub-national data for this study are obtained from published government sources. There are three major findings: (1) there was a general trend towards a reduction in inequity in per-student education spending in these countries; (2) this equalization trend was associated with a variety of financing policies implemented in these countries; and (3) a larger share of regional government funding relative to the share of local government funding, intergovernmental transfer, and the design of school funding formulas are crucial factors for enhancing equity in education funding. The policy towards centralization in education finance system also appears to be an important factor in financial equalization.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".