Bibliographic record
Abstract
The COVID-19 pandemic induced an unprecedented stress around the world. The fear of illness, death, and the loss of loved ones profoundly impacted mental health and produced feelings of helplessness. Simultaneously, measures to contain the virus reshaped social life and created extreme forms of isolation. Moreover, the pandemic engendered a climate of uncertainty, driven by rapidly evolving and conflicting information. These conditions provided fertile ground for the development and rapid dissemination of health-related conspiracy theories, including the belief that COVID-19 was deliberately created and spread, that the virus was being deployed to track people, and that 5G technology was implicated in disease transmission. This chapter reports findings from a multinational survey of American, Mexican, and Canadian respondents, examining individual and social factors associated with belief in COVID conspiracy theories. The study identified three key predictors of conspiracy theories: mistrust in scientific authorities, psychotic-like symptoms, and right-wing political orientation. These three factors collectively accounted for up to a third of the response patterns in in our COVID conspiracies questionnaires, with the American context exhibiting the most pronounced correlation. We propose a model in which these determinants interact as components of a pathway to conspiracy beliefs. In this model, psychological properties of an individual interact with social conditions and cultural influences to engender conspiracy beliefs. Our model aligns with a “situated cognition” approach which emphasizes the pivotal role of an individual&s;s environment in understanding their mental states. The situated approach points to the need for an interdisciplinary dialogue between historical and psychological approaches.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".