La configuration des échanges dans un modèle à biens multiples : quelques paradoxes
Bibliographic record
Abstract
In the framework of a two-good, two-factor model it is evident that the pattern of trade can be inferred from the change in commodity prices resulting from the opening of trade. Thus, if trade increases the relative price of a commodity, we expect that commodity to be exported, while the good whose relative price decreases will be imported. Under certain circumstances however, it may be possible to observe a country importing a commodity even though its free trade relative price is higher than under autarky. The purpose of this paper is to point out that a similar paradox can be established even if we rule out distributional effects of changes in commodity prices on the demand for goods attributable to different tastes. In particular, we focus our attention on a simple three-good, two-factor model with fixed production coefficients. It is well known that when the number of goods exceeds the numbers of factors, a basic indeterminacy exists in the relationship between output levels and relative commodity prices. Our interest lies in establishing that one application of this indeterminacy is that technological characteristics—in particular, the factor intensity ranking of commodities and a country's factor endowment—may result in the reversal of the expected pattern of trade.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".