Investing in electrical manufacturing in France: reinforcing or Americanising the existing structures? A long-term perspective
Bibliographic record
Abstract
French businessmen, contrary to some judgements made recently, were not hostile to American methods and techniques. They borrowed steadily from Uncle Sam. If we go by the electrical manufacturing example, structural handicaps and the will to resist German domination incited France to import technology instead of undertaking its own research before Second World War. After 1945, many observed that the French became suspicious about Americans. However, neither the private nor the public sector went along with this attitude. Both borrowed organisational structures and technology from the United States. Électricité de France even pushed the big French electrical manufacturers to become familiar with American technology. Meanwhile, household appliance producers put into practice various marketing techniques developed by the Americans. But before 1960, these marketing techniques could not create a mass consumption market in France. Proper infrastructure and an extended market were necessary prerequisites. It is only from the 1960s that the catching up of the French became impressive. At that time, Americanisation tended to be generated more and more by the French themselves.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".