Aspects of Political Leadership Relevant to Voters’ Choice and Preferences
Bibliographic record
Abstract
This paper intends to analyse political leadership from the perspective of political behaviour of political leaders. There must be a reason for voters to decide which election candidate to vote. Political leadership as contextually described represent some of the most important elements for voters to decide whether or not a particular leader should be elected as Member of Parliament or State Legislative Assemblies in Malaysia. There are five aspects of political leadership forming leadership characters relevant to voters’ choice and preferences in any given election. They are loyalty, integrity, competency, commitment and resilience. The absence of these characters could render a particular candidacy a fatal. These five characters could be regarded as principle centred of a leader and in the same time the main features that would contribute to the success of an elected political leader. It is not only words best spoken by the candidate but also the campaigner’s success in highlighting these characters to the voters that would open their eyes to vote the best candidate. In some cases, traditionally, at some constituency votes are given to the parties that have been traditionally representing the constituency. To a certain extent it is called the party’s ‘traditional seat’. Now, voters do not only look to a party or the so-called ‘traditional seat’ context. Today voters give high regards to who the candidates are and what are the qualities the candidates have.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".