Contribution of Real Estate Activities and Financial/Insurance Industry Activities to the Formation of Gross Domestic Product
Bibliographic record
Abstract
This research examines and proves the contribution of real estate activities and financial and insurance industry activities to the formation of Indonesia's Gross Domestic Product in 2000-2017. The issue that will be discussed is about the variables that influenced the formation of Indonesia's Gross Domestic Product in 2000-2017. The purpose of this study is to prove the influence of the contribution of real estate activities and financial and insurance industry activities to the formation of Gross Domestic Products in Indonesia. To analyze and prove the hypothesis an empirical test was carried out in the form of Q Square prediction with Smart PLS 3.1 on the magnitude of the influence of the activity sub-sector. The method used in data collection is a combination of secondary data derived from Asia Development Bank data. The method used to analyze data is the time series data method. The results showed that Real Estate Activities is an independent variable that does not have a positive and significant effect on the formation of a total Gross Domestic Product. It can be seen from the statistical t value of 1,800> from the t table value of 1,964 and this can be proven by the original sample value of 0.073 and the significance of 0.073> 0.05 which means that Real Estate Activities has no positive effect and significant to the formation of total Gross Domestic Product. Variable Financial and Insurance Activities are independent variables also do not have a positive and significant effect on the formation of total Gross Domestic Product. This can be seen from the statistical t value of 0.401> from the t table value of 1.964 and this can be proven by the original sample value of 0.689 and no significance of 0.005 = 0.05 which means that Financial and Insurance Activities have no effect positive and significant towards the formation of total Gross Domestic Product.
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.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".