Alternative Risk: A Diagnostic and Canadian Anti-Vaccine Case Study
Bibliographic record
Abstract
This thesis builds on a growing body of interdisciplinary risk scholarship that is taking place across the humanities and sciences. It combines Ulrich Beck’s sociological concept of “risk society”, legal scholar Dayna Nadine Scott’s “risk frame” as a Foucauldian “governmentality” and the techniques of the professional writing discipline of “risk communication” with multi-modal rhetorical analysis to show that “risk” is more than a deliberative discussion of statistics and probabilities: it is a multi-dimensional form of argument that has become a topos, or persuasive “place,” in our social discourse, one where we find arguments about preventing catastrophe ... or where we find arguments for all kinds of other purposes. I argue that this complex rhetorical practice is vulnerable to capture by “alternative risk”: risk communications that adopt the conceptual and formal features of risk discourse to exploit their audience’s risk anxieties. In a context of increasing concern about the volume and impact of disinformation, the concept of “alternative risk” offers a framework for diagnosing patterns and structures of disinformation, which I apply in a Canadian anti-vaccine case study, Stop the Shots in Kids. Mapping this anti-COVID vaccine campaign to the “alternative risk” framework reveals (1) how it uses the stylistic and conceptual features of risk communication alongside rhetorical strategies characteristic of the “alt-right” to advance conspiracy theories and other forms of mis- and dis-information in a manner that makes them difficult to distinguish from legitimate COVID-19 risk communications, and (2) how it uses the risk of vaccination as a “place” to argue about COVID-19 restrictions, mitigation practices such as masking, and the trustworthiness of government and other institutions. The case study, and the other examples included in this thesis highlight that alternative risk is not a “fringe minority” issue, but something of mainstream and ongoing importance in our daily lives.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".