Good Government Governance as a Moderator in Achieving Sustainable Development Goals in Indonesia
Bibliographic record
Abstract
The goal of this study was to examine and assess the factors that affect Sustainable Development Goals (SDGs) study in Indonesia.Based on the context in this research, just the case in Indonesia, Economic Growth, the Human Development Index, and the Environmental Quality Index were the factors utilized in the study to assess the ability to fulfill the Sustainable Development Goals (SDGs).The effect of the Good Government Governance (GGG) variable as a moderating variable was also included in this study.The data used in this study were annual statistics from all provinces in Indonesia, namely 34 provinces from 2018 to 2021.The Warp-PLS 7.0 Structural Equation Modeling (SEM) software was used to analyze the hypotheses test in this investigation.The study's findings revealed that Economic Growth and Human Development Index have a substantial effect on the SDGs, however, the Environmental Quality Index variable had insignificant results.Furthermore, the Good Government Governance (GGG) variable considerably moderates the effect of economic growth, the Human Development Index, and the Environmental Quality Index on the Sustainable Development Goals (SDGs).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".