Bibliographic record
Abstract

 
 
 This exploratory study investigates the links between conspiracy theories, wellness influencers, and COVID-19 misinformation. The study reviews literature that contextualizes conspiracy theories and New Age Spirituality: a framework coined as ‘conspirituality.’ The role of influencers is identified, particularly alternative health influencers, in spreading information about COVID-19. Specific case studies of conspiracy theorists and wellness influencers who have spread misinformation about COVID-19 are used as examples to demonstrate the themes of misinformation. Next, Instagram as a platform is evaluated—specifically, the algorithmic features which enable the spread of COVID-19 misinformation—presenting some solutions and further considerations for policy decisions. Ultimately, this research shows that COVID-19 illuminated a series of existing elements which converged to create an atmosphere whereby young women are exposed to misinformation about COVID-19 by engaging with wellness influencer content on Instagram. This trend can be explained by web-based conspirituality discourse, the gendered social media landscape, and inadequate policies for managing misinformation online.
 
 
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".