Achieving Sustainable Development Goals (SDGs) among the South Asian Countries: Progress and Challenges
Bibliographic record
Abstract
This article analyses sustainable development goals based on selected indicators to capture the progress of SDGs among the South Asian countries. The selected indicators are used to explore countries achievements of SDGs since its adoption in 2015 and challenges of achieving specific SDG. This study adopts various methods including trend analysis, ranking and comparative analysis to analyse the progress of SDGs among the countries. The findings reveal that although SDG1 is progressing, still one third of the world poor population lives in south Asia where India (37.2%) was found highest poverty headcount based on $3.20/day followed by Nepal (33.4%) and Bangladesh (33.2%). The findings also portray that majority south Asian countries spend less than 4% of their GDP on education and health which hindrances the progress of SDG indicators. Moreover, many countries are still far reaching from the environmental sustainability indicators such as CO2 emission per capita, air pollution and forest coverage. Overall, though the countries have achieved some positive progress in particular SDG but majority SDGs including 1, 5, 8, 11, 14-17 remain challenges for achieving the target. Therefore, this study suggests to promote policies and initiatives targeting specific SDG by the countries for achieving the SDGs by 2030.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".