Hybrid factories in the United States : the Japanese-style management and production system under the global economy
Bibliographic record
Abstract
1. Introduction: The Japanese-style Management and Production System in the U.S.A (TETSUJI KAWAMURA) 1.1 JAPANESE AND THE U.S. PRODUCTION SYSTEM AND THEIR VICISSITUDES IN THE UNITED STATES -THE SIGNIFICANCE OF THE HYBRID MODEL ANALYSIS 1.2 HYBRID MODEL AND THE EVALUATION CRITERIA 2. The Japanese Management System and Corporate Strategies (Hiroshi Itagaki) 3. Hybrid Analysis of Japanese Transplants in the U.S.A 3.1 GENERAL FEATURES OF THE JAPANESE TRANSPLANTS IN THE U.S.A. -INTER-REGIONAL AND INTER-TEMPORAL COMPARISONS (HIROSHI ITAGAKI/ WOOSOEK JUHN). 3.2 AUTOMOBILE INDUSTRIES 3.2.1 AUTOMOBILE INDUSTRIES IN NORTH AMERICA (HIROSHI KUMON) 3.2.2 JAPANESE AUTOMOBILE FIRMS IN NORTH AMERICA (SHINYA ORIHASHI) 3.2.3 AUTO ASSEMBLY (KUNIO KAMIYAMA) 3.2.4 AUTO PARTS AND COMPONENTS (KATSUO YAMAZAKI) 3.3 ELECTRONICS INDUSTRIES 3.3.1 ELECTRONICS ASSEMBLY (TETSUJI KAWAMURA/YANSHU HAO) 3.3.2 OTHER ELECTRONICS (HIROSHI ITAGAKI) 4. Specific Cases of Hybrid Factories in the United States 4.1 TOYOTA INDIANA (KUNIO KAMIYAMA) 4.2 TOSHIBA (TETSUO ABO) 4.3 MINEBEA (HANSEN CORPORATION) (ZHIJIA YUAN) 4.4 GM LANSING (TETSUJI KAWAMURA) 5. Situations and Cases in Mexico and Canada 5.1 AUTO AND ELECTRONICS INDUSTRIES AND THE MAQUILADORA IN MEXICO (KOJI SERITA) 5.2 HONDA IN CANADA AND MEXICO (HIROSHI KUMON) 5.3 FORD HERMOSILLO (JORGE CARRILLO AND YOLANDA MONTIEL) 5.4 DELPHI MEXICO (JORGE CARRILLO) 6. Conclusion and Prospects (Tetsuji Kawamura)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".