Approaches for Human Capital Measurement with an Empirical Application for Growth Policy
Bibliographic record
Abstract
This study aims at providing a theoretical concept of human capital and its measurement that have been continually developed for decades and empirically testing the casual relationships between human capital, measured by educational spending, and economic growth of Thailand. The methodologies are through literature synthesis and qualitative analysis as well as time-series quantitative analysis; the data is annually collected during the period 1980 – 2010. The finding indicates that human capital can be defined in different frameworks because its definition changes over time. Nonetheless, owing to a key attribute, it should be defined on a broader view and its spillover effects should be taken into account as well. The other finding is that the standard approaches to human capital measurement are cost-based, income-based and output-based. The empirical results suggest bidirectional causality between human capital and economic growth of Thailand but much clearer for the causation running from human capital to growth. As such, the policy implication is that if the government aims to achieve the long-run economic growth, both increasing the educational opportunity and improving the quality of education are imperative.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".