Bibliographic record
Abstract
This is an easy-to-read book that explains how and why Japan industrialized rapidly. It traces historical development from the feudal Edo period to high income and technology in the current period. Catch-up industrialization is analyzed from a broad perspective including social, economic and political aspects. Historical data, research and contesting arguments are amply supplied. Japan’s unique experience is contrasted with the practices of today’s developing countries. Negative aspects such as social ills, policy failures, military movements and war years are also covered. Nineteenth-century Japan already had a happy combination of strong entrepreneurship and relatively wise government, which was the result of Japan’s long evolutionary history. Measured contacts with high civilizations of China, India and the West allowed cumulative growth without being destroyed by them. Imported ideas and technology were absorbed with adjustments to fit the local context. The book grew out of a graduate course for government officials from developing countries. It offers a comprehensive look and new insights at Japan’s industrial path that are often missing in standard historical chronicles. Written in an accessible and lively form, the book engages scholars as well as novices with no prior knowledge of Japan.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 1.000 | 0.988 |
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; both teacher heads agree on what is shown here.
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".