Twentieth century models for the twenty‐first century: models of fast growing firms and industries in the twentieth century for fast growing firms and industries in the twenty‐first century
Bibliographic record
Abstract
Purpose This paper aims to present a general review of the circumstances of America and Japan's rapid corporate, economic and industrial development in the twentieth century. Design/methodology/approach The approach considered and evaluated how the circumstances of America and Japan's growth might apply to China and India, two of the fastest growing economies of the twenty‐first century. Findings The findings suggest that both America and Japan might be considered exceptional cases and, as such, neither one might be regarded as a good model for emulation. However, the circumstances of Japan's rapid growth appear closer to those of contemporary China and India and on that basis the authors suggest that Japan might be a better model for emulation. Originality/value The American model is too novel and unlikely to be imitated, replicated or repeated whereas Japan's high population density, agrarian origins, state assisted and administered development, adaptation and hybridization of local and imported methods and technologies, kinship, pseudo‐kinship and locality based business groupings, and rapid, come‐from‐behind charge toward industrialization, urbanization and international emergence, all suggest that Japan offers a more relevant and useful development model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".