The hidden dynamics of the USA-Mexico trade relationship: a partial export data decomposition approach
Bibliographic record
Abstract
This study employs a unique methodology to uncover the hidden dynamics of the USA-Mexico trade relationship under the United States-Mexico-Canada Agreement (USMCA) agreement. The conventional bilateral trade balance (BTB) only considers total export data, which may need to be revised for testing the J-curve hypothesis since countries (such as the USA) also re-export to their partners (e.g., Mexico). To address this, the study decomposes total export data into re-export data and domestic export data and proposes two new forms of J-curve hypothesis testing: the partial-domestic-J-curve hypothesis BTB and the partial-re-export-J-curve hypothesis BTB. The study's empirical findings suggest that the partial methodology should be used for asymmetric J-curve hypothesis testing in the USA-Mexico trade. The findings also indicate that Mexican consumers are more sensitive to changes in the value of the peso for US domestic products than re-exported products, and they purchased more US domestic products than re-exported products during the COVID-19 pandemic.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".