Factors Affecting the Implementation of Good Corporate Governance in Control of Operation and Finance
Bibliographic record
Abstract
This study aims to examine the effect of independent variables of inflation rate (X1Inf), install capacity consumer consumptive (X2ICKs), and install capacity productive consumer groups (X3ICPr) to the dependent variable of GCG implementation in controlling efficiency of operating and financial (YGCGEf), to prove the hypothesis, this research used linear regression model and its calculation using SPSS software. The result of this study found that the inflation rate variable (X1Inf) has a positive effect and does not affect the dependent variable of GCG implementation in controlling the operation and financial efficiency (YGCGEf). While the variable Instal capacity consumer consumptive group (X2ICKs) and install capacity consumer productive group (X3ICPr) significant effect on the dependent variable GCG implementation in control of operating and financial efficiency (YGCGEf). The X2ICKs variable has a negative effect and X3ICPr has a positive effect on the dependent variable of GCG implementation in controlling the efficiency of operation and financial (YGCGEf). This research recommends that management policy of PT. PLN (Persero) prioritize variable X2ICKs and X3ICPr, because both of these variables are significant so that significant effect on the dependent variable of GCG implementation in controlling the efficiency of operating and financial (YGCGEf).
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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 | 0.012 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".