Factor Analysis of Paddy-Field Consolidation: Case Study of Iran
Bibliographic record
Abstract
<p>Land consolidation is a strategy for the development of Iranian rice-growing regions. The most important targets of the program are reducing rice farmers' expenses and increasing their income. The object of this article is to conduct a factor analysis of Iranian paddy-field consolidation. The research was conducted in the form of a survey study. The data was collected from188 farmers participating in a farm-development program in Guilan province, sampled using a stratified random sampling method. The reliability of the questionnaire was calculated using a Cronbach alpha coefficient (alpha &gt;0.78) for different sections after conducting a pilot study. Factor analysis for farmers with rice fields in projects showed that five factors explained 63.92% of total variance. These factors were: 1) social, 2) instructional, 3) environmental, 4) economic and 5) institutional effects. Social effects alone, the factor with the greatest effect, explained 34.84% of total variance.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".