Competitiveness of the manufacturing Sector In Mexico, The United States And Canada 2006-2022: Advantages, Disadvantages And Current Trends
Bibliographic record
Abstract
This research seeks to analyze the competitiveness of the manufacturing sector in Mexico compared to the United States and Canada from 2006 to 2022 in the different industries that make it up to identify the advantages, disadvantages and trends. Official databases were consulted to correlate the information through statistics; specialists in the sector (Organization for Economic Cooperation and Development, Mexican Institute of Competitiveness, World Bank, Statistics Canada and the U.S. Bureau of Labor Statistics); reports and studies. The most productive sectors for Mexico compared to the United States and Canada are: food, beverages, tobacco, paper, non-metallic products and transport equipment. The least productive: leather and leather, furniture, textiles and automotive. And the stable ones: wood, metal products, machinery and basic metals. The main advantages: Nearshoring, geographical location, logistics and labor. The disadvantages for Mexico are high dependence on the United States, disruption, technology transfer and inflation. And trends: wage increases, sustainability, turning suppliers into partners, new technologies (cloud, 5G and AI), redesigning work and work culture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".