Market Facilitation Program: Impact on Nebraska Corn and Soybean Producers
Bibliographic record
Abstract
In July 2018, President Trump imposed a first round of 25-percent tariffs on Chinese electronics and high-tech equipment including automobiles, computer hard drives, and LEDs. The tariffs were imposed on roughly $34 billion worth of imported goods. In August 2018 additional 25-percent tariffs were imposed on $16 billion worth of Chinese exports to the United States and in September tariffs on $200 billion worth of Chinese exports to the United States were added. (Bradsher, 2018). The Trump Administration has also imposed automobile, steel and aluminum tariffs on imports from Canada, the European Union and other countries. In response to the first two sets of tariffs, China placed retaliatory tariffs on $60 billion dollars of imports from the United States matching the value of the goods subjected to U.S. tariffs. According to Bradsher (2018), Chinese imports from the United States are so much smaller than U.S. imports from China that the Chinese government was unable to match the magnitude ($200 billion) of the latest round of U.S. tariffs, applying tariffs only to an additional $60 billion worth of U.S. goods. The Chinese tariffs target sensitive U.S. sectors including several agricultural industries. In the initial round of retaliation, for example, U.S. soybean exports to China—which account for more than 50 percent of total U.S. soybean exports—were hit with 25 percent tariffs. Swanson et al. (2018) reported predictions that the tariff would cause the average annual 2018 soybean price to fall from an expected $9.70 per bushel if no tariff were imposed to $8.85 per bushel.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".