Bibliographic record
Abstract
As poverty remains rampant, the Millennium Development Goals have been established to address what is one of the most chronic challenges to growth and development. With numerous governments and international organizations adopting these eight international goals focused on combating both the income and non-income dimensions of poverty, it is imperative to measure the performance toward and success of the MDG initiative. By exploring the interplay among poverty, growth and inequality, this study evaluates the progress of 90 developing countries in attaining the income poverty target contained in the first MDG (MDG1), focusing on developing Asia. To help inform future, results-based development policies, it also examines whether adopting the MDGs has contributed to income poverty reduction by measuring the growth elasticity of poverty, controlling for growth. Through an achievement index developed by Kakwani in 1993, the study estimates that an annual poverty reduction of around 2.77% between 1990 and 2015 is needed for countries to attain MDG1. Across developing Asia, half of the 22 countries included in the study will definitely attain the target and 46% are “likely” to achieve it. Can such gains in poverty reduction be ascribed to the espousal of the MDGs? The study finds that improvements in poverty elasticity are statistically insignificant in the post-MDG period, implying that the acceleration of poverty reduction has been mainly due to economic growth and not the adoption of the MDGs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".