Analisis ketimpangan pendapatan pedagang kaki lima di Kota Kuala Tungkal
Bibliographic record
Abstract
This study aims to analyze the socio-economic characteristics and inequality of income of street vendors in Kuala Tungkal City. The data used is sourced from surveys at street vendors. Data were analyzed descriptively and used the gini coefficient and lorentz curve. The results of the study found that street vendors in Kuala Tungkal City were characterized by: 1) Dominated by men with the highest proportion of ages between 26 - 45 years. Merchant education is relatively varied ranging from never going to school to graduating from high school. More than two thirds of them are married with the number of family dependents dominated by 1-4 people. In terms of business location, almost three-quarters of them operate in the market area and only about a quarter of them are trying along the main road. The capital of street vendors in the city of Kuala Tungkal is relatively varied from Rp. 50,000 to over Rp. 1,000,000, with more than half having the same capital or less than Rp. 550,000. Furthermore, in terms of income, most street vendors have an income of Rp. 151,000 - 250,000; 2) Using the method of class three class, the inequality of income of street vendors in the low category with a gini ratio of 0.22772. Furthermore, using the class five class method, the inequality of income of street vendors is also in the low category with a gini ratio of 0.2
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".