Surfing the Waves of Infodemics: Building a Cohesive Philippine Framework against Misinformation
Bibliographic record
Abstract
The sudden exponential increase in information accompanying COVID-19 has presented significant barriers to effective health communication. In a country where 76 million active social media accounts originate, this infodemic due to the pandemic has exposed inadequacies in Philippine information systems. As such, this paper aims to present the infodemic in the Philippine context, analyze existing frameworks countering minsinformation, identify problems, and propose solutions for misinformation. A comprehensive review of existing policies was conducted by inputting keywords in known databases, and analyzing literature, laws, and social media policies in the Philippines. The analysis has showed that (1) the current uncentralized system presents difficulties in mobilizing experts; (2) the older demographic is a neglected population amid high risk for misinformation; (3) individual passivity in searching for legitimate sources puts people at higher risk; (4) current legal frameworks insufficiently characterize and delineate misinformation and disinformation, leading to concerns on implementation and human rights. To address this, evidence recommends (1) creating a centralized government institution, representative of various sectors, to serve as the source of understandable and reliable scientific information; (2) strengthening current legal frameworks, with an emphasis on education, due process, and human rights; (3) ingraining a culture of fact-checking within the Filipino psyche via stakeholder engagement. Clear roles and responsibilities, along with active stakeholder engagement, are needed to build individual resilience against misinformation and strengthen veritable institutions that aid the country in responding to future health crises.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.016 | 0.056 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".