On the Rural Land Consolidation Procedure Legislation's Perfection in China
Bibliographic record
Abstract
The rural land consolidation procedure legislation’s scientific degree is lower in China; it can’t guide the rural land consolidation process systematically and normatively. The integral research on legislative practice includes two aspects, i.e., legislative system and legislative content. To do the theoretical research based on legislative text is the most relevant research methods. To perfect the rural land consolidation procedure legislation in China by the formulation or modification of relevant legislation is very necessary and possible. Key words: Rural land consolidation; Procedure; Legislative system; Legislative content Resume Le diplome scientifique de la legislation fonciere rurale de la procedure de consolidation est le plus bas en Chine, il ne peut pas guider le processus de consolidation des terres rurales systematiquement et normativement. La recherche integree sur la pratique legislative comporte deux aspects, a savoir, le systeme legislatif et le contenu legislatif. Pour faire de la recherche theorique basee sur le texte legislatif est des methodes de recherche les plus pertinents. Pour parfaire la legislation fonciere rurale de consolidation procedure en Chine par la formulation ou la modification de la legislation pertinente est tres necessaire et possible. Mots cles: Le remembrement rural; Procedure; Systeme legislatif; Contenu legislatif
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".