Achieving Socioeconomic Development Fuelled by Globalization: An Analysis of 146 Countries
Bibliographic record
Abstract
Globalization is embedded in socioeconomic development at the glocal scale (local to global). Drawing up from Kate Raworth’s Doughnut economics framework coupled with UN Sustainable development goals, we interrogate the relationship of globalization for socio-economic development (2000–2017). Here we have applied the Spearman correlation and data envelopment analysis to assess the efficiency of nations in ‘converting’ their level of globalization towards achieving socio-economic development along with trends of reaching the just operating space for 146 countries. Then, we calculate improvement targets and identify trends among income categories (World Bank). We have also analyzed the Malmquist productivity index for 34 large economies to understand spatiotemporal trends of change in efficiency and their contributing components (2001–2015). We have found that productivity change was mostly influenced by technical progress. A large group of countries are moving towards crossing desired thresholds; however, some are harnessing globalization efficiently to get assistance. It is possible to maintain dual achievement. However, some of the countries are lagging in one or both aspects. Most countries could attain just operating space even with their existing level of globalization. Our findings reveal the importance of the dual achievement: using contemporary features (such as globalization) for the benefit of socioeconomic 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".