Bibliographic record
Abstract
Aim/Purpose: This study explores the dissemination of COVID-19 conspiracy theories in Canada in order to create a model for verifying conspiracy theories. Background: The study combines empirical and conceptual research. Methodology: Three Canadian cases of conspiracy theories dissemination were developed via observation and content analysis, and an exploration of ontology, epistemology, and logic of conspiracy of conspiracy theories was conducted. Contribution: The study contributes to understanding conspiracy theories related to COVID-19 and possibly beyond. Recommendations for Practitioners: Findings can help in detecting COVID-19 conspiracy theories. Recommendations for Researchers: Findings can help understanding the nature of conspiracy theories. Impact on Society: Identifying COVID-19 conspiracy theories helps in managing public health communication and informing, uncertainty, and mass behavior during public health emergency. Future Research: More research on COVID-19 is needed in different social contexts inter-nationally as well as on validating the proposed model for verifying COVID-19 conspiracy theories.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.032 | 0.022 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".