Examining the United States Balance of Trade
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
This paper aims to explain the United States balance of trade—an integral component of the domestic economy and a political focus as of late. The balance of trade is investigated using an Ordinary Least Squares time-series regression model using the following explanatory variables: the trade-weighted US Dollar index, the unemployment rate, the US-Mexico-Canada Agreement (USMCA), and China’s Most Favored Nation (MFN) status. Regression results indicated statistical significance for all four of the explanatory variables at the 1% level, though the trade-weighted US Dollar index had a positive coefficient with the dependent variable, contrary to what economic theory would suggest. Newey-West standard error corrections were made to account for autocorrelation. The model produced strong results which might illuminate the future effects of a shift toward protectionist trade policy.
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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) | 0.001 | 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