The Evolution and its Economic Impact of Wealth Identification in China from the Perspective of Household Sector's Balance Sheets
Bibliographic record
Abstract
Wealth identification is one of the important concepts in generalized virtual economy. It reflects as household's financial assets and non-financial attest, so the evolution of it is analyzed according to the change of asset structures of household's balance sheets. This paper prepares household sector's balance sheets in 1994-2009 and studies the evolution of wealth identification in China by comparing it to ones in Canada, Japan, UK and Australia. The paper analyzes economic impact of this evolution by the thermal optimal path method. The research gives some suggestion to deal with the changing wealth identification. The findings is as follows. Housing, securities and deposits were top three of Chinese household's wealth identification over the past 20 years. The type of wealth identification is basically the same in the different time and space, but the ratios of these are very different. Land is the most important one in the material wealth identification. However, the virtual one is widely dispersed, which is the major areas of generalization and shift of wealth identification. The change of growth rate of ratio of land and housing to asset significantly affects the change of CPI and GDP growth rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".