Bibliographic record
Abstract
In this chapter we analyse the evolution of the North American economic integration (formerly NAFTA, renamed USMCA in 2020) based on the cooperation between Canada, Mexico, and the United States. We used the common methodological framework explained in Chapter 2 , which includes a composite index using principal component analysis. During the analysed time period (1994–2019) several crises influenced our analysis. Immediately after the foundation of NAFTA, the Peso crisis hit Mexico, in 2000 the burst of the dotcom bubble, and in 2008 the Global Financial crisis. These all influenced the data-set, which is a limitation of our analysis. The data show stagnation in the composite index of Canada and the United States after the 2008 Global Financial crisis. Meanwhile, Mexico’s integration took up at the same time period. By integrating the supply chains of North America, the region managed to remain competitive with its Asian rivals, which may not have happened had NAFTA not been implemented. Considering the three decades of North American integration, the member states don’t seem to be willing to deepen the level of integration further. The main reason for this is the turbulent domestic politics of the United States.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".