Structural Equation Modeling Applied to Socioeconomic Indicators in the Production of Sugarcane, in the State of Goiás
Bibliographic record
Abstract
Agribusiness has played a strategic role for Brazil's development with the challenge of sustainable agriculture. It is proposed to determine, through Structural Equation Modeling (SEM), the validity and effects of the relationships between socioeconomic factors of the sugarcane production system in Quirinópolis, providing subsidies to the decision-making process of agricultural establishments. The research methodological approach was quantitative, applying techniques of normality statistics, hypothesis and multivariate analysis without statistical significance (P <0,05). A path diagram model was developed that presented structural quality adjustment and its validated explanatory equations, obtaining relevant R2. The results demonstrate that the Equation 1 (IBCcane = 0.02Rcane - 0.75ICcane – 0.46ISVO + 0.35ISPS + error) is explained in 73.7% of its variance (R2), in the Equation 2 (ICcane = 0.59ISVO – 0.45ISPS + 0.35SizeEstablis + error) successor vocation affects 42% on production costs and in the Equation 3 (Rcane = -0.40 AgroDistance – 0.16ISPS + error) the distance between farm and agribusiness influences 72% on the proposed revenue mix. The SEM analysis verified that social factors influence the economic factors that compose the sugarcane production system studied. The path diagram proved that the influence track relative to the costs in the proposed model is more representative than revenue for the economic results of rural sugarcane establishments.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".