Global value chains, development and emerging economies:Concepts, measurements and trade-offs
Bibliographic record
Abstract
In recent decades, profound changes in the structure of the global economy have reshaped global production and trade and have altered the organisation of industries and national economies into global value chains (GVCs). As GVCs became global in scope, more intermediate goods were traded across borders, and more imported parts and components were integrated into exports. In 2009, world exports of intermediate goods exceeded the combined export values of final and capital goods for the first time. New governance structures reinforce the organisational consolidation occurring within GVCs and the geographic concentration associated with the growing prominence of emerging economies as key economic and political actors. Emerging economies are playing significant and diverse roles in GVCs. During the 2000s, they were simultaneously major exporters of intermediate and final manufactured goods (China, South Korea, and Mexico) and primary products (Brazil, Russia, and South Africa). However, market growth in emerging economies has also led to shifting end markets in GVCs, as more trade has occurred between developing economies (often referred to as South-South trade in the literature), especially since the 2008-09 economic recession. China
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.026 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".