Bibliographic record
Abstract
This study aims at testing the causal relationship between human capital via the government spending share on education and economic growth using cross-country evidence and investigating the relationship pattern between such human capital – growth and the level of economic development based on 30 country data. The study employs a standard approach through uniting root test and Granger causality test. The data is annually collected during the periods 1983 – 2012, totaling to 30 observations. The finding indicates that for both developing and developed countries, education human capital cannot explain much the economic growth and vice versa. In addition, from the relationship pattern between human capital – growth and the economic development level neutrality is the most commonly found pattern for both developing and developed countries. However, we see somewhat difference between them in terms of causation running from growth to human capital. That is, the number of developed countries is almost double as compared to the developing ones. This gives rise to a policy implication for developed countries in that it should put more emphasis on the government education spending share to GDP since it can help boost human capital in the long run.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.004 | 0.001 |
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".