The impact of OECD's Development Assistance Committee (DAC) AidCommitments for Education on Human Development in Asian Countriesand its implications for textile industry
Bibliographic record
Abstract
Education and health are considered a cornerstone for obtaining targeted development in any society. Moreover, both sectors promote prosperity greatly. In this changeable epoch, people are thought out as the real wealth of any nation and this wealth with good human capital serves the economy very efficiently and productively. This research study aims to analyse how Development Assistance Committee (DAC) aid commitment for education along with institutional quality is effective for the human development of selected Asian economies. A panel data set over 2011–2018 is used for this analysis in Asian countries. GMM results show a significant and positive relationship between aid commitment for education and the human development of these economies. A more interesting result is that financial development seems to boost up human deployment in the selected Asian economies. The development of the textile industry is significantly influenced by education, especially considering the effects of OECD's Development Assistance Committee (DAC) Aid Commitments for education on human development in Asian countries. There is a dire need to reconsider more allocation of resources and aid to education and health to utilize these inflows at the maximum level for targeted development.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".