On the Injustice of the Policy for House Purchasing and Settling--Caution: Two Secessions of 2010 Lowering the Limitations of House Purchasing and Settling
Bibliographic record
Abstract
Restarting Policy for House Purchasing and Settling is a good means that the local governments deal with the problems such as weakness of property market, declination of purchase will of the customers and downturn of the stock market, so the NPC and CPPCC sessions suggest to deduce the limitations of Policy for House Purchasing and Settling in the small and middle cities and towns. But this policy brings more disadvantages (such as overload of city ecological environment, public power’s interference with the market rules, accelerating the unfairness of society and its legitimacy, etc.) than advantages (increase of the local governments’ financial revenue, improvement of urban landscape and offering more jobs). The disadvantages are much more than the advantages that can make short-term positive effects, which proves the injustice of the policy, so it should be terminated as soon as possible.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.024 | 0.040 |
| Insufficient payload (model declined to judge) | 0.003 | 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".