Economic Growth as Mediation of Regional Own Source Revenue, Investment and Asset Management on Labor Absorption and Social Welfare in Regencies/Cities in Riau Islands Province
Bibliographic record
Abstract
This study, entitled Economic Growth as Mediation of Regional Own-source Revenue, Investment and Asset Management on the Labor absorption and Social welfare of Regencies/City in Riau Islands province. This study was conducted at the district / city located in the province of Riau Islands. There are Exogenous constructs (X) namely income, investment, and asset management variables. There are Intermediate variables (Y) namely economic growth and labor absorption. There is an endogenous construct namely social welfare. There are 12 hypotheses tested by using path analysis or SmartPLS.The results in this study show that the 1st hypothesis as the regional Own-source Revenue has a significant effect on economic growth. Hypothesis 2 Regional Own-source Revenue has a significant effect on labor absorption. Hypothesis 3 Regional Own-source Revenue significantly influence the social welfare. Hypothesis 4 Investment has significant effect on economic growth. Hypothesis 5 Investment has non-significant effect on labor absorption. Hypothesis 6 Investments significant effect on the social welfare. Hypothesis 7 Asset Management has non-significant effect on economic growth. Hypothesis 8 Asset Management has a significant effect on labor absorption. Hypothesis 9 Asset Management has significant effect on the social welfare. Hypothesis 10 Economic growth has non-significant effect on labor absorption. Hypothesis 11 The economic growth has significant effect on the social welfare. Hypothesis 12 Labor absorption has significant effect on the social welfare.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".