An Examination of the Viability of a Class Action for Adverse Events Following Covid-19 Immunizations
Bibliographic record
Abstract
Abstract: The COVID-19 pandemic has forced governments around the world to take extraordinary measures to mitigate the virus’s deadly impact. These measures include the approval, procurement, and distribution of vaccines to citizens. This paper examines the likelihood of a class action against the Canadian federal government being certified under British Columbia’s Class Proceedings Act, RSBC 1996, c 50 for Adverse Effects Following Immunization [AEFI] caused after receiving a dose of a government-approved vaccine. Intended as a thought experiment, this essay is not a case study of an actual action, but rather a study of how the Act and the courts would approach such a case, were it brought. It explores what the proper cause of action would be, what potential class definition would be chosen, what the proposed common issues would be, and the viability of alternative procedures for resolving claims for AEFI. This has been done through an examination of the currently available data relating to the vaccine approval process, and a review of relevant jurisprudence.
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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 | 0.101 | 0.182 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".