Production sharing in Latin America and East Asia
Bibliographic record
Abstract
In this paper we empirically examine the extent and properties of production sharing in Latin and North America as well as that in East Asia. In 2006, exports of parts and components from Latin and North America constituted 29.7% of the region’s exports of manufactured goods to the world. Both exports and imports of parts and components were declining shares of trade in manufactured goods or trade in all goods. A large amount of trade in parts and components in the region was with members of NAFTA, particularly the United States. Imports from East Asia and from China were increasingly important. There was a relatively thick production network of parts of motor vehicles in Latin and North America, followed by networks of parts of telecommunication equipment and electronic components. But the network was primarily within the United States, Mexico and Canada, with Brazil also playing a role. For East Asia, the motor vehicle parts network was not as significant, but the electronic components network was much wider and deeper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".