Bibliographic record
Abstract
An infodemic of false information and conspiracy theories has followed closely in the wake of the ongoing COVID-19 pandemic, exacerbating the public health disaster. In order to curb their spread and counter their effects, conspiratorial beliefs must be catalogued and understood. Drawing on examples from social media video and audio sharing platforms, we provide a non-exhaustive list of conspiratorial beliefs related to the COVID-19 pandemic, and categorize them into three groups: A) beliefs concerning the motivation of the conspirators, including bringing down a rival nation-state, bringing about planetary depopulation, and/or imposing global tyranny; B) beliefs concerning the nature of the COVID-19 disease, including that the disease is made-up, that its impact is exaggerated, that it is caused by a bioengineered virus, and/or that it is caused by a non-viral agent; and C) beliefs concerning the public health response, including that masks and vaccines are harmful to health, and/or that vaccination is an insidious way to track and control the population. We conclude by reflecting on the necessity of tracking and understanding the continuously evolving epistemic ecosystem of pandemic-related conspiracist beliefs in order to implement effective strategies to “quarantine” harmful conspiracy theories and “vaccinate” individuals against conspiracism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".