Regulating Online Pandemic Falsehoods: Practices and Interventions in Southeast Asia
Bibliographic record
Abstract
Online falsehoods proliferated with the outbreak of COVID-19, leading to conspiracy theories and vaccine hesitancy in Southeast Asia. In this chapter, we investigate the effects and enforcement of falsehood regulations on the democratic freedoms in nine countries. Broadly, we compare (1) the laws governing online falsehoods on mobile instant messaging services (MIMS) platforms and other social media; (2) the forms of enforcement across the region based on V-Dem’s Pandemic Backsliding Index, Digital Society Index (DSI), human rights reports and news media agencies. Specifically, we also compare how the respective governments in (3) Cambodia, Indonesia, Malaysia and Thailand enforced online pandemic falsehood regulations. Our findings show that while laws governing online falsehoods are necessary interventions to minimise distrust of public health information, abuses occur when vaguely defined laws obfuscate and authorities interpret and enforce laws in discriminatory ways. If left unchecked, the arbitrary expansion of the state’s policing power is likely to lead to democratic backsliding in the post-pandemic era.
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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 | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".