Bibliographic record
Abstract
Firms in regional trade areas choose whether to comply with rules of origin (RoO) or pay a tariff penalty. Stricter content requirements initially expand regional part sourcing, but contract it when set at levels above a threshold, analogously to the Laffer curve for taxes. We calibrate the model to fit part cost shares for autos sold in North America. The effects of the 75% RoO imposed in 2020 depend on the relevant tariff and the ability to relocate assembly. With fixed assembly locations, the higher RoO reduces employment in all three countries for cars, where tariffs are low. For trucks, the 25% US tariff induces more compliance in Canada and Mexico, increasing employment in those countries. With the option to relocate assembly, higher RoOs redistribute employment to the US, but Canada and Mexico lose more, leading to a half percent decline in North American employment for both cars and trucks.
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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 | 0.008 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".