India–United States Agricultural Trade Under the America‐First Agenda
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 This article examines India–United States agricultural trade under the America‐First agenda, highlighting trade patterns, tariff structures, and potential impacts of United States trade policies. Total agricultural trade remains modest at $7 billion annually, with India imposing higher tariffs (average 39%) than the United States (5%). The study models two scenarios: a 25% United States tariff on steel and aluminum, which reduces India's exports by $1.32 billion and contracts GDP by $154 million, and broader United States tariffs affecting Canada, Mexico, and China, causing mixed impacts but leading to $1.56 billion GDP growth for India. Additional risks include tariff escalation, nontariff barriers, climate change, and shifting geopolitics. Potential areas of mutual benefits are identified.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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