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
President Trump announced a new tariff of 25 percent on steel imports and 10 percent on aluminum imports on March 8, 2018. One objective of these tariffs is to protect jobs in the U.S. steel industry. They were introduced under a rarely used 1962 Act, which allows the government to impose trade barriers for national security reasons. Although the tariffs were initially to apply to all trading partners, Canada and Mexico are currently exempt subject to NAFTA negotiations, and implementation of the tariffs for the European Union, Argentina, Australia, and Brazil has been paused. South Korea has received a permanent exemption from the steel tariffs and will instead be subject to a quota of 70 percent of its current average steel exports to the United States. In this post, we consider how the steel tariffs could affect U.S. trade and employment. We focus on steel since the steel industry employs about three times as many workers as the aluminum industry, although qualitatively our conclusions apply to both. We argue that the new tariffs are likely to lead to a net loss in U.S. employment, at least in the short to medium run.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it