Information disorder, the Triumvirate, and COVID-19: How media outlets, foreign state intrusion, and the far-right diaspora drive the COVID-19 anti-vaccination movement
Bibliographic record
Abstract
Information disorder has become an increasing concern in the wake of the 2016 US presidential election. With the state of the COVID-19 pandemic rapidly evolving in all facets, the vaccination debate has become increasingly polarized and subjected to a form of politics based around identity markers such as nationality, ethnicity, gender, and ideology. At the forefront of this is the COVID-19 anti-vaccination movement that has gained mainstream attention, leading to conflict with pro-vaccinationists. This has paved the way for exploitation by subversive elements such as, foreign state-backed disinformation campaigns, alternative news outlets, and right-wing influencers who spread false and misleading information, or disinformation, on COVID-19 in order to promote polarization of the vaccine debate through identity politics. Disinformation spread sows confusion and disorder, leading to the erosion of social cohesion as well as the potential for real-world conflict and violence. As a result, the article below will generate further understanding of the modern-day spread of disinformation, the strategies and tactics utilized by state and non-state actors, the effects of its exposure, and the social-psychological processes involved in its spread and resonance. Furthermore, in countering this phenomenon, this article recommends a collaborative framework involving emphasis on critical media literacy skills, citizen participation, and development of counter-offensive capabilities towards state-backed information operations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".