Bibliographic record
Abstract
Abstract In this study a multi‐country model of trade is developed that captures the role of country‐specific communications network interconnectivity, which enhances trade in intermediate business services. The number of countries connected to internationally interconnected networks is found to determine the structure of comparative advantage. That is, countries with interconnected networks have a comparative advantage in the good that requires business services provided via networks. In connected countries, producers of that good benefit from the efficient transmission of business services. This research also demonstrates that countries whose country‐specific networks are not connected to the interconnected networks may become worse off as the result of trade. JEL Classification: D43, F12 Interconnectivité des réseaux de communication et commerce international Ce mémoire développe un modèle de commerce international impliquant plusieurs pays qui cerne le rôle de l’interconnectivité des réseaux de communication de chaque pays. Cette interconnectivité enrichit les flux de commerce dans les services intermédiaires d’affaires. Il appert que le nombre de pays connectés aux réseaux internationalement connectés détermine la structure de l’avantage comparatif. Ce qui veut dire que les pays qui ont des réseaux interconnectés ont un avantage comparatif dans le bien qui requiert les services d’affaires fournis par les réseaux. Dans les pays connectés, les producteurs de ces biens bénéficient de la transmission efficace de ces services d’affaires. La recherche montre aussi que les pays dont les réseaux nationaux ne sont pas connectés aux réseaux interconnectés peuvent voir leur situation s’empirer en conséquence du commerce international.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".