On the Impact of China's Accession into WTO on Its Social Insurance Legislation——And on Further Improvement of China's Social Insurance
Bibliographic record
Abstract
Prior to the adoption of Social Insurance Law of People's Republic of China,Chinese social insurance legislation was featured with low legislation level,fragmentation,legislative content lagging behind the development of the times,as well as heavy emphasis on urban areas than rural areas.The impact of China's accession into WTO on the adoption of Social Insurance Law is mainly reflected in three areas:changes of international trade situation accelerated the legislation process;changes in the legal system of other areas affected the social insurance legislation;relevant WTO rules promoted legislation in China's social insurance administration system.Take pension insurance as an example,the impact of China's accession to WTO on Social Insurance Law for this specific type of insurance could be mainly found in the following areas:promoting national legislation for the expansion of pension coverage,helping to push forward the establishment of a multi-layered pension insurance system,promoting,to a certain extent,the inter-regional transfer of pension relationship.After the promulgation of Social Insurance Law, there is still some room for further improvement:the legal system needs further perfection,equality is to be further enhanced,Chinese national character is to be further explored and established.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".