Issues in 13th General Election: A Case Study of Kedah, Malaysia
Bibliographic record
Abstract
This study was conducted to identify the issues that are expected or predicted in the 13th general election. The study was conducted in the state of Kedah, Malaysia as a case study. The population is comprised of people eligible to vote in the election to 13 in the state of Kedah and the sample of 500 respondents. The study took into account six focus areas in Kedah which cover four parliamentary constituencies and divided into six in the state of Kedah. They are Jerlun constituency (DUN Kota Siputeh) and Kubang Pasu (DUN Jitra and Bukit Kayu Hitam) which represent the ruling party-BN, while for the opposition, this study chooses the Parliament of Merbok (DUN Tanjung Dawai dan Bukit Selambau) and Pendang (DUN Tokai). The findings show that the quality and standard of living are the most important issues that are expected to be manipulated by the public during the election campaign as compared to other issues. Voters are so concerned by the rising cost of living in recent years due to the rising of prices, including fuel prices, and studies show they (people) feel pressured on the rising of living cost. As such, the contesting parties must update this problem and try to give priority to the development of physical infrastructure such as physical development, economic and social in one to match that quality of people's living standards and this should be done carefully.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".