Drivers and Roadblocks for Maquiladoras’ Journey to Sustainability
Bibliographic record
Abstract
U.S. manufacturing firms are leaving operations in China because the Covid-19 pandemic, high tariffs, rising labor and transportation costs, and long distance have started to develop a near-shoring trend, making Mexican Maquiladoras their main destination. Consequently, this decision has the potential to significantly harm sustainability in the U.S. and Mexico because currently 10% of worldwide trade pollution and emissions have been produced by Canada, Mexico, and the United States. Not only are social stakeholders in the Mexican territory the main recipients of this negative effect, but also are those in the US, given maquiladora proximity to American soil. Because of the limited research on sustainability practices in Maquiladora firms, I investigate what attributes enable or impede the sustainability management practices (SMPs) adoption by Maquiladoras. I offer a conceptual framework of attributes – maquiladora firms’ operational mode, perceived benefits, position in the supply chain, product market destination, and current information communication technologies (ICTs) status – and propositions drawing from neo-institutional and natural resource view theories.
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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 | 0.000 | 0.000 |
| 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".