The China Triangle: Latin America's China Boom and the Fate of the Washington Consensus
Bibliographic record
Abstract
In less than a quarter century, China has gone from one of the poorest to one of the largest economies in the world. As China grew from a rural economy to the largest industrial powerhouse since the Industrial Revolution, it demanded more and more steel for factories and new cities, copper for electronic wires, petroleum for cars and manufacturing plants, and soybeans to feed people and cattle in a country with an increasing standard of living and diversified diet. Many Latin American countries rode China's coattails and prospered. From the turn of the century when China entered the World Trade Organization until 2013, Latin America served as a strategic location that supplied China with these primary commodities and more, allowing it to log one of the region's most impressive periods of economic growth in a fifty years. In The China Triangle, Boston University political economist Kevin P. Gallagher argues that Latin American nations have little to show for riding the coattails of the "China Boom" and now face significant challenges for the next decades as China's economy slows down and transforms itself in a variety of ways. While the region saw significant economic growth due to China's rise over the past decades, Latin Americans saved very little of the windfall profits it earned while the region saw a significant hollowing of its industrial base. What is more, commodity-led growth during the China boom reignited social and environmental conflicts across the region. Scholars and reporters have covered the Chinese expansion into East Asia, Southeast Asia, Australasia, Africa, the US, and Europe. Yet China's penetration Latin America is as little understood as it is significant. Gallagher provides a clear overview of China's growing economic ties with Latin America and points to ways that Latin American nations, China, and even the United States can act in order to make the next decades of China-Latin America economic activity more prosperous for all involved.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".