Reprogramming Japan: The High Tech Crisis Under Communitarian Capitalism
Bibliographic record
Abstract
Reprogramming Japan: The High Tech Crisis Under Communitarian Capitalism. By Marie Anchordoguy. Ithaca, NY: Cornell University Press, 2005. 257p. $39.95. Over the last 20 years, Japan has passed from wunderkind of global capitalism to problem child, troubled by recession and stagnation. Marie Anchordoguy's Reprogramming Japan joins the growing body of analysis diagnosing this sad falling off, focusing on the crisis of the high-technology sectors where silicon samurai once seemed to reign supreme. In Anchordoguy's view, the cause of the problem is “communitarian capitalism.” This is a capitalism that depends heavily on state direction—governmental support for select large firms, a social contract assuring citizens permanent employment, regular wage increases, and union–management deals for labor peace. This, she suggests, is something verging on socialism. Although it has “all the trappings of private property and profit-making institutions,” the dynamism of the market is constrained by a system that “favor[s] social stability over efficiency” (p. 7). It is, in her view, “quasi-capitalism” (p. 7). Communitarian capitalism, she argues, laid the basis for Japanese success from the 1950s through the 1970s, when global economic conditions were positive, technological trajectories were clear, and foreign products could be reverse-engineered. But in the 1980s and 1990s, intensified competition, transnational outsourcing, and fiercely enforced intellectual property rights made this system a fetter on the very forces of production it had fostered.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".