ECONOMIC GROWTH IS VIEWED FROM THE OINT OF VIEW OF LIFE EXPECTANCY, LARGE TRADE PRICE INDEX, BUSINESS TENDENCY INDEX, CONSUMER TENDENCY INDEX AND ZAKAT RECEIPTS, INFAK AND AIMS
Bibliographic record
Abstract
This research aims to identify and analyze the effect of life expectancy, wholesale price index, business tendency index, and consumer tendency index on economic growth with zakat, infaq and almsgiving receipts as intervening variables in Indonesia. Method: The method used in this study is a quantitative method with the type of associative research, population and sample in this study is Indonesia's Economic Growth Quarter I 2005 –Second Quarter of 2022 Central Statistics Agency. Result: Based on research on life expectancy, the wholesale price index, the business tendency index,and the consumer tendency index have a significant positive effect on economic growth in Indonesia. The results of this study indicate that the receipt of zakat, infaq, and alms has a significant positive effect on Indonesia's economic growth. Conclusion: It can be concluded that it can contribute to the government, zakat managers, the general public, both academics and economic actors and the general public so that they understand more about the linkage of life expectancy, wholesale price index, business tendency index, consumer tendency index to economic growth
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".