B40 Group Income Household Trend in Malaysia
Bibliographic record
Abstract
Income inequality is crucial issue in the Malaysian economy. This issue has a great impact especially on the B40 group income household because of the rising cost of living today. Therefore, modelling of income data is done to look at income pattern of B40 group in Malaysia. Household income data for Malaysia in year 2007, 2009, 2012, 2014 and 2016 have been used in this study. The income distribution used in this study is a two-parameter distribution of Weibull, Log Normal, Fisk and Gamma. This study uses only two parametric distributions to suit the income data because the simplest model is better than the complex model. The best distribution selection is performed with the fitting of statistical distribution through maximum likelihood estimation (MLE) method. Goodness of fit test has been done to model B40 household income data. The best model for each year used to predict the average income in the future by using regression method. Weibull distribution is the best model for B40 household income data. The study also shows that the average income of the B40 group in the future will increase. Therefore, this study was conducted to assist B40 group to be more sensitive to the Malaysian economy and plan their income wisely.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".