Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| 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 it