Issues in transferring technologies to maquiladoras
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
In the last 35 years, the Mexican side of the USA-Mexico border has witnessed a very significant growth in the manufacturing sector. With a modest beginning of only a dozen plants in 1965-66, the border area now boasts over 4000 manufacturing establishments along the 2,000 mile border, employing millions of workers directly or indirectly. While the majority of these establishments, commonly referred to as "Maquiladora Industry", is US-owned, investment from other locations such as Europe, Japan, South Korea, Canada and Taiwan, is growing. The low direct labour cost, availability of a large labour pool and proximity to the largest consumer market in the world (USA) are the major reasons for increased and sustained investments. A large number of Fortune 500 companies now own a maquiladora. The revenue from these has become a very substantial part of these companies' bottom line. However, the productivity and quality standards of maquiladoras continue to suffer as a result of high worker turnover rates, lack of skills, poor training and low wages. For companies to compete in the global market, it is critical that not only the negative impacts of these factors on production, costs and efficiency be minimised but also that the latest in manufacturing technology be employed. This paper reviews the issues that are relevant in transferring manufacturing technology to maquiladoras and, thereby, making them competitive in the global market. The paper also discusses environmental issues resulting from the production of hazardous waste by maquiladoras.
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 | 0.001 | 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 it