Bibliographic record
Abstract
This paper selects the indexes of currency position,securitisation rate,ratio of dependence on foreign trade,manufacturing ratio,foreign portfolio investment and others to do cluster analysis on the economic patterns of 9 countries by the clustering methods of system.The results were attained that the countries can be divided into three sorts of economies: USA and Britain belong to fictious economies because of their high currency position,securitisation rate and foreign portfolio investment;Canada,Germany and France belong to semi-fictious economies because their indexes are in the middle level,and Japan belongs to semi-fictitious economies because of its lower fictitious level but higher than semi-fictious economies;Russia,China,India and other developing countries belong to non-fictious economies because of their high ratio of dependence on foreign trade and manufacturing ratio.China has been the largest non-fictious economy.The paper provides the following suggestions: to maintain superior position of the real economy,to exploit funds and technology of fictious economies,to develop trade with semi-fictious economies,and to accelerate the transformation of economic development mode and the revision of industrial structure.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".