Electoral Management of Digital Campaigns and Disinformation in East and Southeast Asia
Bibliographic record
Abstract
Election interference is a problem in digitized elections around the world. Given East and Southeast Asia's dense social network, electoral integrity is a growing concern. Yet, few studies focus on this region's regulatory approaches to data-driven campaigns or disinformation threat. This article addresses this by proposing an electoral management digital readiness (EMDR) index to compare the readiness of the ten electoral management bodies (EMBs) in East and Southeast Asia to respond to digital disruptions. The aim is to take stock of the new and amended laws and provide a composite index based on four key criteria, namely, the (1) type of electoral management model; (2) presence of specific or new regulations governing online campaign and disinformation; (3) confidence in the rule of law; and (4) technological readiness of the digital economy. Based on available legal statutes and cross-country indicators, this study finds the EMBs in Singapore, South Korea, Japan, Taiwan, and Thailand to have a high level of digital readiness; Malaysia, the Philippines, and Indonesia to have a medium level; and Cambodia and Myanmar to have a low level. A key finding is that a multi-pronged regulatory approach that involves different stakeholders is necessary to improve the timeliness of fact-checking and removal of malicious content, rather than relying on state-led initiatives to improve online electoral governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".