A Model of Green Business Predicted Green Economy through Inspiration of Public Mind
Bibliographic record
Abstract
The research objectives were to validate the causal model of Green Business (GB) Predicted Green Economy Perception (GEP) through Inspiration of Public Mind (IPM) of undergraduate of Rajabhat Mahasarakham University.The findings illustrated that GB and IPM can predict the variation of GEP with 79.00 percent.GB had the most direct effect on GEP with an effect 0.48, subsequence was IPM with an effect 0.45.Moreover, GB had effect on IPM with an effect of 0.40 and be able to predict the variation of IPM with 80.00 percent.The causal model of GB effect with IPM and GEP was confirmed the proposed model and it was fitted with all observed variables consistent with criteria of Chi-Square/df value with less or equal to 2.038.It was less than or equaled to 5.00 (X 2 /df < 5.00).RMSEA (Root Mean Square Error Approximation) equaled to 0.042 (RMSEA < 0.05) and RMR (Root Mean Square Residual) equaled to 0.028 (RMR < 0.05) including index level of model congruent value of Goodness of Fit Index (GFI) equaled to 0.94, and Adjust Goodness of Fit Index (AGFI) equaled to 0.93 which are between 0.90-1.00.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".