Bibliographic record
Abstract
Will the US sustain its economy after the tariff war with China, or will the economy regress? This paper offers a conceptual framework, based on the tenets of New-Keynesian theory, to answer this question. I anticipate that the tariff will have a positive effect on the GDP of the US economy in the short run while prices will rise. When adding the most recent reforms of interest cut by the Fed to 1.75% in September (2019) the model concludes a better outcome. Followed by an expansionary monetary policy by reducing the interest rate, the aftermath of the tariff war on China seems to have a positive impact on the US income and productivity. Obviously, some critics to the Trump Administration indeed shed light on the curtailed global and US social welfare that is caused by the inflationary effect of the tariff war, in addition to the deteriorating conditions for some trading sectors in the US which would certainly lead to unemployment. But the benefits to the US economy that are translated by the New-Keynesian theoretical framework show a positive impact on US production, employment, and GDP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".