Industrial Clusters and Nearshoring in Mexico: A Strategic Framework for Economic Repositioning
Bibliographic record
Abstract
The objective of this work is to make known the importance of clusters through nearshoring. In Mexico, it is part of several trade agreements, such as the T-MEC (Treaty between Mexico, the United States, and Canada), which facilitates international trade and provides tariff advantages. This may attract companies looking to benefit from these agreements. Mexico has a qualified and competitive workforce in terms of costs. Education and technical training are constantly growing, providing companies with trained human talent. Nearshoring can boost the creation and expansion of industrial clusters in different regions of Mexico. Clusters are geographic concentrations of companies and organizations related to the same sector. These clusters foster innovation and collaboration, increasing synergies and operational efficiency. Mexico has a diversified economy with key sectors such as manufacturing, automotive, technology, and aerospace. Nearshoring can encourage the growth of clusters in these sectors, promoting innovation and technological development. Mexico has shown economic stability and has policies to support industrial and technological development. The government has implemented programs to encourage foreign investment and infrastructure development, which can be attractive to companies seeking to establish themselves in the country. Establishing new businesses and expanding existing ones can create jobs and stimulate local economic growth. Clusters encourage collaboration and the exchange of ideas, which can lead to technological advances and greater competitiveness. Regional Development: The creation of clusters can help develop less urbanized regions, better distributing economic opportunities throughout the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".