On the Feasibility, by Means of Customs Duties, of an Entirely (or Almost Entirely) Made-in-the-USA Automobile
Bibliographic record
Abstract
Abstract One of the objectives of the Trump administration’s economic policy is to revitalize the American industrial fabric and create a large number of high-paying blue-collar jobs. However, the main instrument used to achieve this goal – tariff protection – is a point of contention. We discuss the relevance of the recently introduced policy for an emblematic sector: the automotive industry. The latter operates highly integrated production chains where intermediate products frequently cross borders to circulate within a ‘Big Factory’ encompassing production sites located mainly in Mexico, Canada, and the USA, but also in other countries. The imposition of a 25% tariff on finished cars and their parts could lead to significant disruptions for consumers and producers alike. The lessons learned from the automotive sector retain much of their relevance for other areas of the US economy. In the absence of a nationwide adequate solution, the lot of displaced workers could be improved through place-based workforce transition programmes limited to disadvantaged areas. Industrial policy measures targeting disadvantaged communities and regions could also be envisaged. In this case, however, it would be necessary to deploy a variety of instruments adapted to the circumstances and to take into account, as far as possible, the interests of trading partners in order to avoid conflicts.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".