Toward economic growth: Income distribution in the era of the COVID19 pandemic in east Kalimantan province
Bibliographic record
Abstract
The technological era is a dilemma in the economic growth of a region. The policy of economic development, at least, contains two main objectives to be achieved, namely growth and equity. These two goals are usually in conflict with each other. That is, if growth reaches a high level, then equity reaches a decline so that the conscious effort to create a balance is one of the goals of development. Growth to increase income per capita is an effort in progress to increase output (through the use of factors of production with or without technological change) continuously in the long run, which is always associated with population growth. Because with high output growth coupled with high population growth, the growth of output will become a new problem, so efforts to overcome unemployment are also a crucial part of development. Equitable distribution of fixed income is one of the critical issues faced by an economy. Doing a real business venture so that the rent is more evenly distributed is an essential responsibility of an economic system. The development of an economy will cause changes that are not always good due to the use of labor. This sometimes causes the number and level of unemployment to increase, along with population growth. Finally the paper considers whether there is any evidence of government expenditure, Private investment and poverty rates on Income distribution in East Kalimantan Province is Significantly influenced but Economic is not Growth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".