Bibliographic record
Abstract
ABSTRACT New trends are now taking place within manufacturing industries led by multi-national corporations (MNCs). Globalization and liberalization together with the information technology (IT) revolution has accelerated “fables” industry in the network economy, i.e. outsourcing production processes and global parts procurement by MNCs. As a consequence of this, the primary function of the MNC has changed from that of manufacturer to ‘service’ provider by outsourcing production processes to foreign contract manufacturers (CMs). NAFTA in fact mutated Mexico into a production platform toward the US and Canada as well as Latin American countries. We can observe these dramatic changes, for instance, in Guadalajara in Mexico, now called the “Silicon Valley in the South”. Since MNCs use their brand names to sell products, their business function becomes close to that of the fashion industry. They market their products in the same way as Gucci and Chanel sell products of original design carrying their brand names. Therefore, product design and marketing become highly important for MNCs to achieve success in business while domestic providers have been left behind for their parts and components supply in this new global supply chain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".