Structural Equation Modeling Applied to Socioeconomic Indicators in the Production of Sugarcane, in the State of Goiás
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it