Competitive features of country associations based on the Global Competitiveness Index: the case of the United States – Mexico – Canada Agreement
Bibliographic record
Abstract
Modern architecture of the world economy is determined not only by the indicators of development of individual national economies and not only by their individual efforts, but also by coordinated efforts of several countries, such as trade agreements. One of such agreements that should lead to freer market relations, fairer trade and sustainable economic growth in the region, better resolution of international disputes, environmental protection, intellectual property protection, etc., is the United States – Mexico – Canada Agreement (USMCA). The aim of this paper is an attempt to analyze the level of global competitiveness of intergovernmental associations (agreements) as influential participants in the international market. To do this, the concepts of “competitive power of the country” and “competitive power of international integration groups” are compared with the concept of firm competitiveness. The competitiveness of the country association was analyzed through the example of the USMCA based on The Global Competitiveness Report 2019 and The Global Competitiveness Index 4.0. used in it. The paper also examines the global challenges and obstacles affecting the level of competitiveness and competitive advantage that each country receives when participating in an international integration agreement. This analysis helps explain real competitive processes in the global economy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".