Bibliographic record
Abstract
The cluster plan I n the early 2000s, Japan had still failed to fully recover from the economic doldrums. Unemployment rates had surpassed the US jobless by 1999 (Porter et al . 2000). In 2003, new business creation remained paltry. A 2003 study, the Gem 2003 Executive Report (2003) indicated that Japan was less entrepreneurial – on a variety of firm and individual level measures – than all of the advanced industrial countries, save Russia. Of the world's forty major economies, most were at three entrepreneurship levels (high, moderate, low) based on measures including new firm start-ups, innovative output, and the like. Highly entrepreneurial countries included Chile, Korea, and New Zealand. Most countries were moderately innovative, such as Canada, Finland, Singapore, the UK, and the USA. The least entrepreneurial countries were France, Japan, and Russia. Japan failed at both individual- and firm-level innovation and entrepreneurship, making it among the least entrepreneurial countries in the world (see figure 4.1 and table 4.1). (See also appendix 1: in the Gem 2003 Executive Report 2003; Porter 1990, 1998; Porter et al . 2000.) The Ministry of Economy, Trade and Industry (METI) is spearheading the Japanese government's current efforts to fix its innovative and entrepreneurial problems. In 2001, METI launched its “Cluster Initiative,” culminating in 2002 in a package of policies called the “Cluster Plan.” The “Plan” has become the most ambitious and comprehensive METI plan since its 1960s bet on heavy industry (Inoue 2003).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".