On the Feasibility, by Means of Customs Duties, of an Entirely (or Almost Entirely) Made-in-the-USA Automobile
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it