THE ANALOGY OF SUSTAINABLE COMPETITIVENESS OF SAARC AND G-SEVEN NATIONS
Bibliographic record
Abstract
From the visionary thinking the sustainable development is must for the survival of future generation and humans. For the sustainable development the economy should have sustainable competitiveness. Therefore the main objective of this study is to compare the sustainability competitiveness of SAARC and G7 nations. To meet this objective the data has been collected from the official reports on global sustainability competitiveness index. The main five pillars of this index viz. natural capital, resource efficiency, intellectual capital, governance performance, and social capital are compared between nations and group of nations. To get statistically significant results the independent sample t-test, One-Way ANOVA, and Post-hoc Tukey test has been performed. In case of SAARC nations the results indicates that the Pakistan, Bangladesh, India, and Sri Lanka are having lower sustainability competitiveness as compared to the Maldives, Bhutan, and Nepal. In case of G7 nations the results indicates that the US, Italy, and Canada are having lower sustainability competitiveness as compared to the Japan, France, UK, and Germany. In further investigation of the data it is observed that the sustainability competitiveness of G7 nations is higher as compare to the SAARC nations. The results of this study will be helpful to the nations, NGOs, and several world organisations working for sustainable development of the nations and universe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".