Bibliographic record
Abstract
Japan’s competition law, the Act on Prohibition of Private Monopolization and Maintenance of Fair Trade, came into force in July 1947. This means Japan’s competition law is the world’s third oldest competition law after Canada and the USA. Competition laws and legal regulations have been spreading globally. While jurisdictions with competition laws were only Canada, the USA, Japan, Germany, and the current European Union in 1960, by 1990, competition laws had spread to 25 jurisdictions, including countries in Europe, Australia, parts of Central and South America, etc. Since then, the number of jurisdictions establishing competition laws continued to increase: in 2000, 86 jurisdictions had competition laws, and in 2017, the number reached to 120. Japan’s economy has reached a maturity stage, and demands for existing goods and services are at the point of market saturation. What will drive an economy forward under such circumstances is innovation. Steve Jobs, the founder of Apple, stated, ‘People don’t know what they want until you show it to them.’ This can be understood to mean that the supply side drives the economy. The implication is that, by proposing new products and services from the supply side, potential demand is stimulated and actual demand increases.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.092 | 0.046 |
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".