Malaysian Young Voters’ Voices in the New Political Landscape
Bibliographic record
Abstract
The 12th General Election in March 2008 had changed Malaysia’s political landscape in significant ways. A series of unexpected events happened and for the first time in history, the ruling party, Barisan Nasional (BN) failed to obtain the two third majority votes. This ‘political tsunami’ had influenced people from various backgrounds especially those in the political parties to probe further into the reasons behind the changes. After a series of ‘post mortem’ and discussions, political leaders now began to realise about the importance of moving out from their ‘comfort zone’ and responding immediately to the signals from the public. By ignoring people’s voice particularly the young voters who made up more than forty percent of the total voters, political parties were digging their own graves. Thus, this paper was written in order to identify young voters’ trends and preferences in choosing their candidates to represent them in the new political landscape. The findings of this research showed that young voters preferred to choose candidates based on their profile, images and personal characteristics, visions and missions as well educational backgrounds. Most importantly, the candidates must also be ‘clean’ in all aspects. Hopefully, the findings will provide political parties better understanding of the young voters’ needs and want in order to ensure their parties’ survival since this group of voters is the deciding factor of the future Malaysian political landscape.Keywords: by- election; young voters; candidate factor; Permatang Pasir
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".