The Geographic Scope of Opposition Challenges in Malaysia’s Parliament
Bibliographic record
Abstract
During the long rule of the BN (Barisan Nasional) coalition prior to 2018, Malaysia's parliament, the Dewan Rakyat, was largely absent from analyses of political contestation between the ruling government and its opposition. Nevertheless, during this period, opposition MPs were active users of available legislative tools such as parliamentary questions, offering a rich source of data about their priorities and political positioning. This article investigates how MPs from the opposition used parliamentary questions to build their public reputations, and whether those reputations were built around attention to local, subnational, or national issues. It uses an original dataset of over 37,000 oral questions submitted by MPs in Malaysia's House of Representatives from 2008 to 2018. I find that opposition MPs were more likely to focus on local and subnational reputation-building compared to ruling government MPs. These differences were especially pronounced in East Malaysia, where opposition MPs were heavily oriented towards local infrastructure and issues of state underdevelopment and autonomy. I explain these findings as a result of the opposition's need to build a constituency reputation in lieu of access to state resources, as well as a greater responsiveness to local- and region-specific grievances. This focus both complements, and differs from, how Malaysia's MPs used extra- parliamentary strategies to cultivate personal and party reputation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".