Battling Against COVID-19 Infodemic in Indonesia: A Sociocybernetics Perspective
Bibliographic record
Abstract
As Indonesians collectively fight against the COVID-19 pandemic, the nation is simultaneously combatting the rampant spread of misinformation related to COVID-19. This phenomenon is often referred to as an ‘infodemic,’ defined by the World Health Organization (WHO) as the mass spread of information, factual or nonfactual, during a disease outbreak. In this article, we employ the methods of sociocybernetics analysis to examine the COVID-19 infodemic in Indonesia. We divide this paper into two sections. In the first section, we lay out the current state of the problem in Indonesia -how misinformation has challenged the post-pandemic recovery and changed the dynamics of Indonesian society at all levels, ranging from individuals to the society as-a-whole. In the second section, we propose a model, based on the approach of sociocybernetics, by which we propose to assess this challenge not just as a single entity but as a continuous, looping process, from the conception to the impact it has caused at all levels (micro, meso, and macro) of society. Given the complexity of this issue, we propose to develop an awareness and the education of cybernetics or systems thinking across multiple sectors when dealing with the infodemic in Indonesia.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".