The China Triangle: Latin America's China Boom and the Fate of the Washington Consensus
Bibliographic record
Abstract
"Since 1980, China has evolved from a poor and mostly rural society into one of the largest economies in the world. As it grew into a major industrial power, it demanded enormous amounts of steel for new factories and cities, copper for electronic wires, petroleum for cars and manufacturing plants, and soybeans and cattle to feed its workers. By the 1990s, many Latin American countries were riding China's coattails and beginning to prosper from the new demand. Ever since China entered the World Trade Organization at the turn of the century, Latin America supplied China with more and more of the primary commodities it needs and more. That in turn has produced one the most impressive periods of economic growth on the continent in fifty years. And it was more evenly spread too - a region infamous for its extreme inequality saw it decline by a couple of percentage points over the course of the era. In The China Triangle, Kevin P. Gallagher traces the development of the China-Latin America trade over time and covers how it has affected the centuries-old (and highly unequal) US-Latin American relationship. He argues that despite these opportunities Latin American nations have little to show for riding the coattails of the 'China Boom' and now face significant challenges in the next decades as China's economy slows down and shifts more toward consumption and services. While the Latin American 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 even as 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-especially for America g
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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".