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
The rising prominence of intra-industry trade and huge multinationals has transformed the way economists think about the gains from trade. In the past, we focused on gains that stemmed either from endowment differences (wheat for iron ore) or inter-industry comparative advantage (David Ricardo's classic example of cloth for port). Today, we focus on three sources of gains from trade: 1) love-of-variety gains associated with intra-industry trade; 2) allocative efficiency gains associated with shifting labor and capital out of small, less-productive firms and into large, more-productive firms; and 3) productive efficiency gains associated with trade-induced innovation. This paper reviews these three sources of gains from trade both theoretically and empirically. Our empirical evidence will be centered on the experience of Canada following its closer economic integration in 1989 with the United States—the largest example of bilateral intra-industry trade in the world—but we will also describe evidence for other countries.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.090 |
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