The influence of macroeconomic infrastructure on supply chain smoothness and national competitiveness and its implications on a country's economic growth: evidence from BRICS countries
Bibliographic record
Abstract
This study investigates the intricate relationships between macroeconomic infrastructure, supply chain smoothness, national competitiveness, and economic growth within the BRICS nations—Brazil, Russia, India, China, and South Africa. This study adopts a quantitative approach with cross-sectional data to examine the interrelationships. The research confirms that macroeconomic infrastructure significantly influences supply chain smoothness and a country's economic growth, underscoring the pivotal role of infrastructure development in enhancing supply chain efficiency and fostering economic expansion. However, rejecting hypotheses regarding the direct impact of supply chain smoothness and national competitiveness on economic growth highlights economic growth dynamics' complex and multifaceted nature within the BRICS context. This study emphasizes the need for nuanced, context-specific strategies to address each BRICS nation's unique challenges and opportunities. Theoretical implications call for a more comprehensive theoretical framework considering the contextual factors influencing economic growth within BRICS countries. Practical implications highlight the importance of strategic infrastructure investments and comprehensive policy approaches that extend beyond isolated factors. Despite its contributions, this study has limitations, including simplifying complex economic relationships and needing more country-specific analyses. Future research should explore broader variables, non-linear relationships, and country-specific nuances to understand economic growth in the BRICS group better.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".