Research on the Influencing Factors of Peasants’ Autonomy to Play in Post-earthquake Reconstructions ——A Case Study of Wenchuan Earthquake in China
Bibliographic record
Abstract
In this article, five villages in Sichuan earthquake-stricken area are selected for study as examples. By building Logistic of binary choice models to analyze the influencing factors of peasants’ autonomy to play in post-earthquake reconstructions, authors found there was a positive correlation between education, subjective cognition of reconstruction, willingness to participate, effectiveness of improving the living environment after reconstruction and the autonomy of peasants to participate in post-disaster reconstruction. There was a negative correlation between cognition about the source of reconstruction funds, decision-making subjects of reconstruction and the autonomy of peasants in post-disaster reconstruction. Whether or not to actively participate in post-disaster reconstruction had no significant correlation to gender, age, effectiveness of increase in redevelopment projects, effectiveness of science and technology extension in redevelopment projects, leading subjects of reconstructions, government’s respect for the wishes of peasants, or peasants’ satisfaction for the performances of government in reconstruction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".