B40 Group Income Household Trend in Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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) | 0.000 | 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 it