Bibliographic record
Abstract
Despite being the cornerstone of trade theory, the concept of comparative advantage remains an empirically and operationally weak concept. Typically invoked as the rationale for and of trade proper, comparative advantage is rarely ever described in any detail, let alone measured and tested. This article examines the “problem” of comparative advantage and explores alternatives. Specifically, in light of recent findings regarding the very nature of trade (e.g., its vertical nature [Hummels, Rapoport, and Yi 1998 Hummels, D., Rapoport, D. and Yi, K.-M. 1998. Vertical Specialization and the Changing Nature of World Trade. Federal Reserve Bank of New York Economic Policy Review, 4: 79–99. [Google Scholar]; Hummels, Ishii, and Yi 2001; Lüthje 2005 Lüthje, T. 2005. Vertical Specialization Across Developed Countries. The International Trade Journal, 19(3): 193–216. [Taylor & Francis Online] , [Google Scholar], 2006 Lüthje, T. 2006. Vertical Specialization Between Developed and Developing Countries: A Modification of the Heckscher-Ohlin Model. The International Trade Journal, 20(4): 407–427. [Taylor & Francis Online] , [Google Scholar]] and the increasingly global nature of value chains), the concept of horizontal (sector, good) comparative advantage is abandoned in favor of vertical comparative advantage, defined over individual links or strands of links of a given value chain. Regions/countries (region/country) are assumed to have vertical comparative advantages, not horizontal comparative advantages. For example, 19th century Great Britain had a vertical comparative advantage in processing cotton and silk from its colonies, not in textiles per se (horizontal comparative advantage). Today, Japan has a similar vertical comparative advantage in the transformation of imported intermediate goods. The result is a more complete theory of comparative advantage, one that is testable, amenable to policy analysis, and sufficiently general to include existing approaches as special cases.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".