School Quality, Educational Inequality and Economic Growth
Bibliographic record
Abstract
Realizing the importance of education in developing a country, many governments had begun to pay more attention in improving the education quality in their country. However whether the desired level of education quality is equally distributed is still debated on. On top of that, current literature on which level of education, either basic or tertiary education, brings greater return to the society is still inconclusive. It is not the objective of this paper to answer or add on the debate. On the other hand this paper would like to explore the relationship between school quality, namely at primary education and secondary education, and economic growth. Educational inequality at primary and secondary education would be measured with using the concept of education Gini. Using GDP as the dependent variable and regressing it with Gini coefficient of primary education and secondary education, would be able to show which level of education inequality is significant in explaining the economic growth of a country. Using Malaysian data, for the last 20 years, the relationship between education inequality of different level of education and the economic growth would be postulated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".