DETERMINANTS OF USMCA COMPETITIVE FORCE FORMATION
Bibliographic record
Abstract
Understanding the factors that influence the formation of the competitive force within the United States-Mexico-Canada Agreement (USMCA) is crucial for delineating the features that contribute to the effective functioning and development of its economic competitive landscape. The primary objective of this article is to examine the impact of the interplay between determinants influencing the global competitiveness of member states and international integration groupings on the sustainable economic growth of the USMCA. The article introduces the author's conceptual framework, which presents a comprehensive classification of determinants that shape and enhance the adaptive competitiveness of integrated economic systems. This framework, alternatively referred to as the interpretation of the drivers of the USMCA's global competitiveness, applies to any international integration grouping. The proposed approach advocates the separation of the USMCA's development model as an independent entity, distinct from the model that governs the global competitiveness of individual member states. Notable differences have been identified by evaluating several determinants, including economic performance, government efficiency, business efficiency and infrastructure, on the formation of the global competitiveness of member states within the USMCA. The member states of the USMCA show significant differences in their global competitiveness. It is worth mentioning that the criterion of economic performance stands out as the most influential factor, with all three member states performing better in terms of competitiveness on this criterion. The study's findings underscore that the USMCA's competitiveness is a key feature of its economy. It is evident through the cumulative, synergistic effect of the pooling of global competitive forces among the member states. This amalgamation strengthens the USMCA's position in the global economy. Assessing the global competitiveness of the USMCA is important for delineating the development trajectories of member states within the international integration grouping. It is crucial for the design of a coherent competitiveness policy and the promotion of intergovernmental and inter-union dialogue. It also serves as a valuable indicator or marker of the USMCA's competitive development trajectory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".