Convoy, coalition, and conspiracy: Finding common cause in anti-vaxxer movements
Bibliographic record
Abstract
In this article, we evaluate the online identity building processes that can lead the members of alternative health communities and conspiritualists to embrace positions occupied by the far Right. First, we present the findings of a data scraping and visualization project, drawn primarily from then-Twitter over the first six months of 2022, a period that includes January and February when the Canadian capital Ottawa was occupied by the so-called Freedom Convoy. Drawing on a selective corpus of over 25,000 tweets sourced via seed keywords, our research connects parallel pathways that lead from different starting points to similar end positions–points of no return where new alliances are formed. Second, we conduct a visual content analysis of a series of ten Instagram accounts hosted by female conspiritualists. In our analysis of 400 images, drawn from accounts of a range of influencers in Canada and the United States with roughly 5,000–500,000 followers, we seek to uncover what the visual content created by these influencers reveals about the online wellness-to-conspiracy pipeline. Taken in sum and in relation to cognate studies elsewhere, these two case studies help to illuminate the susceptibility of conspiritualists to the rhetoric and agendas of the far Right.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".