Interpreting Autoimmune Risks in Covid-19 Vaccine Literature: A Two-Phase Qualitative Coding Protocol on Biological Mechanisms and Epistemic Integrity
Bibliographic record
Abstract
The COVID-19 global vaccination campaign was justified as a necessary exit strategy from an unprecedented public health, social and economic crisis. However, concerns about adverse effects post-vaccination - particularly autoimmune reactions - have received comparatively less attention in the peer-reviewed literature. This protocol outlines a two-phase qualitative coding study that builds on a completed scoping review of 109 peer-reviewed articles to evaluate how associations between COVID-19 vaccination and autoimmune disorders are interpreted and framed. Phase 1 will focus on biological plausibility, by extracting mechanistic explanations such as molecular mimicry, bystander activation, and cytokine dysregulation, and assessing whether the mechanistic evidence reported aligns with authors’ conclusions. Phase 2 will focus on epistemic integrity, by applying a typology to analyze the evidentiary consistency between claims about vaccine-related harms and benefits within studies. Quotations will be extracted and analyzed for causal reasoning, rhetorical framing, and evidentiary symmetry. Articles will be double-coded, with inter-rater reliability assessed and adjudicated through discussion. By integrating mechanistic and epistemic analyses, the planned study will provide a framework for evaluating the interpretive standards applied to vaccine safety claims. Rather than reaffirming or rejecting specific biomedical positions, it will document how scientific knowledge is framed, qualified, or selectively emphasized - highlighting interpretive practices that have shaped evidence-based discourse within a climate of perceived urgency and institutional consensus.
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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 | 0.161 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".