Bibliographic record
Abstract
only to that with Canada. The US-Japan trade relationship is often described as unbalanced, and it is true that year by year Japan records a large surplus in its trade with the US, stemming from macroeconomic factors such as high saving rates in Japan and the large savings-investment gap in the US. In this and other respects, imbalance can in a sense reflect structural complementarity. A large share of Japan's bilateral surplus is re-invested by Japanese firms and institutions in American manufacturing industry or in the American bond market. Even so, for many years, the US has been deeply concerned over its substantial trade deficit with Japan and wished for a substantial reduction. Both Lincoln and Schoppa address this objective, but their approaches and methods are quite different. Lincoln is an economist, Schoppa a political scientist. Naturally, the two authors employ different analytical tools. Another difference is Lincoln's practical experience in trade policy, which Schoppa lacks, hence being restricted to looking at the issue from a scholarly viewpoint. Furthermore, Lincoln has a rather broad and general approach, whereas Schoppa looks at very specific questions. The two
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".