Let’s talk... about farmers: Trump’s Trade War and a looming mental health crisis
Bibliographic record
Abstract
Donald Trump’s aggressive stance on trade has led to heavy tariffs on US agricultural products. This has exposed American farmers to a high amount of volatility and work stress. Suicides in America have drastically increased since the late 1990’s, and agriculture has been one of the hardest-hit industries with suicide rates far above the national average. The added stress of Trump’s trade war has the potential to exacerbate the growing rates of depression and suicide among farmers and agricultural workers. The Trump administration’s attempt to ease the strain on farmers with simple monetary compensation has been heavily criticized for its short-sighted nature, and pundits highlight the need for a long-term solution. However, this issue is not limited to the United States and has been observed in several countries around the world, and none have yet identified an effective resolution. A greater response is needed by federal governments to aid farmers and stabilize agricultural markets, as national advocacy groups struggle to provide essential resources to those in need and improve the rates of suicide and depression among farmers.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".