Governing the Covid-19 Pandemic in Malaysia: Shifting Capacity under a Fragmented Political Leadership
Bibliographic record
Abstract
Drawing on a variety of material—mass and social media texts, government reports, and everyday observations—this article examines two interrelated dynamics in Malaysia in 2020–2021: the COVID-19 pandemic's unfolding local trajectory and the short-lived Perikatan Nasional (PN) coalition's governance capacity. Despite political instability resulting from this government's rise to power following internal political manouevrings, it managed to e ectively control a major wave of cases with the help of a centralized healthcare system manned by permanent professional sta and the imposition of coercive measures. Thus, Malaysia's success in "governing" the early phase of the pandemic is arguably attributable to its strong state infrastructure, notwithstanding the untimely unfolding of this political coup. However, an ideal type approach—that is, concern with state capacity—is inadequate in making sense of subsequent failures to control the pandemic after a state election took place several months later. Using Migdal's "state-in-society" approach, this article focuses on the political process of pandemic governance to shed light on Malaysia's shifting state capabilities. Arguably, the resulting shifting responses were mainly shaped by: (1) continuous partisanship; (2) PN's internal fragmentation; (3) PN's complacency in initially "flattening the curve"; and (4) poor governance during the state election.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".