The role of institutions in logistics performance as a new road toward GVC participation
Bibliographic record
Abstract
Participation in global value chains (GVC) is increasingly seen in recent years as the new development challenge by policymakers in many countries. The COVID-19 pandemic recently revealed to the public eye that logistics capabilities are the backbone of the country's competitiveness within the GVC. Nevertheless, studies on logistics performance and GVC are still lacking until today. This paper aims to investigate the role of national institutional quality in the logistics performance-GVC participation nexus, using 68 countries covering 2005 through 2021. The findings of the present study reveal that (a) logistics performance has a positive and significant impact on GVC participation, (b) improving the overall level of governance has a positive impact on GVC participation, (c) the quality of national institutions plays a significant moderating role in logistics performance and GVC participation nexus, (d) the impact of logistics performance on forward linkage, under high governance countries is greater than under low governance countries and (e) the backward linkage is much more sensitive to the time and cost control related to cross boarder and document compliance in countries with poor governance than in countries with good governance.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".