Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article discusses formerly America’s top low-cost manufacturer; the neighbor to the south is repositioning itself to be an advanced industrial player. Colantuoni manages market research for Mexico’s Offshore Group, which develops maquiladoras for firms that want to manufacture in Mexico. Maquiladoras import goods from the United States without paying taxes or tariffs, manufacture or assemble them into products, and export them back across the border. Today, Mexico is making more complex and sophisticated products, as well as goods that require fast turnarounds and customization. It has used its advantages to retain business even in industries—computers, telecommunications, and appliances—that seemed a natural fit for China. Management in general is also a consideration for manufacturers. Mexico’s physical proximity to the Unites States and Canada, and the shared business culture of North America make management easier. In many ways, competition from China has been good for Mexico. It has spurred it to move into engineering and manufacturing higher value-added products.
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.
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 | 0.000 | 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 | 0.001 |
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 it