Proceedings of the global conference on education and research: Volume 5
Bibliographic record
Abstract
The human capital approach, introduced inthe 1960s, has been pivotal in connecting education and skills to personalincome, prompting a shift in public expenditure to view education as aneconomic investment rather than merely a cost. This theory underscoreseducation’s critical function in the economy, suggesting that it couldpotentially alleviate poverty, inequality, and social exclusion—especiallywithin a knowledge-based economy. However, the post-pandemiclandscape in the United States has revealed significant increases insocioeconomic inequality and social exclusion, challenging the optimistic projectionsfor the future workforce. The disruption caused by the COVID-19 pandemichighlighted the connection between human capital, gross domestic product, andper capital income, affecting nations across the income spectrum. The rise ofremote work has underscored universities' need to equip individualswith the skills to thrive alongside artificial intelligence in evolving jobmarkets. The World Bank, as a leading development finance institution, plays asignificant role in shaping education reforms and policies in this context. Thisstudy uses World Bank data to evaluate the importance of education in supporting tomorrow's workforce, aligning with the human capital approach and advancements in labor-substituting technologies. It will explore thefundamental question of education’s role in building human capital through asystematic literature review of 40 publications and a time series analysis ofthe World Development Reports from 1978 to 2024. The article provides insightsinto how the World Bank influences education policy formation while examiningthe relationship between education, human capital, and economic growth,ultimately contributing to a broader understanding of educational strategies inthe face of evolving economic challenges. Keywords: Human Capital, Skilled Workforce, Education, World Bank, Remote Work, Economic Growth, Poverty
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.270 | 0.148 |
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".