Bibliographic record
Abstract
In this paper the two workhorse theories of international trade under imperfect competition – Krugman’s taste for variety model and Brander’s strategic intra‐industry trade model – are integrated into a single analytical framework. A quadratic utility function allows for a nesting of these two theories by postulating a consumer taste for variety over differentiated products, where the extent of product differentiation is linked to the intensity of strategic interaction among firms. The model yields intuitive predictions on the effects of the degree of product differentiation on the volume of trade and on the composition of the gains from trade under imperfect competition. JEL classification: F12 Différenciation de produits, concurrence et commerce international. Ce mémoire intègre deux théories connues du commerce international en régime de concurrence imparfaite – le modèle du goût pour la variété de Krugman et celui du commerce intra‐industrie de Brander – en un seul cadre analytique. Une fonction d’utilité quadratique permet d’encadrer ces deux théories en postulant que le consommateur a un goût pour la variété dans une gamme de produits différenciés où le degré de différenciation est reliéà l’intensité de l’interaction stratégique entre les entreprises. Le modèle engendre des prévisions quant aux effets du degré de différenciation des produits sur le volume de commerce international et la composition des gains résultant de ce commerce en régime de concurrence imparfaite.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".