Potential Conscientious Objection to mRNA Technology as Preventive Treatment for COVID-19
Bibliographic record
Abstract
In the context of mass vaccination campaigns, the most widely used vaccines in Western countries are based on messenger RNA (mRNA). Some countries have imposed mandatory vaccination and many others have required a vaccination passport to access public transportation and many activities, producing systemic discrimination, social exclusion, segregation, and stigmatization against non-vaccinated individuals. This paper aims to present several scientific uncertainties on which, conscientious objectors to mRNA injections as a preventive treatment for COVID-19, could rely. Scientific data are presented on mRNA vaccines, which consist in mRNAs wrapped in lipid nanoparticles. Never used as a prophylactic drug, artificial mRNAs delivered to our cells forces them to express, against their nature, a biologically active viral protein. Unlike a drug produced in a pharmaceutical factory and formulated at a known dose and a well-defined protein product profile, the mRNA vaccine acts as a pro-drug encoding for the viral Spike protein of the virus to be produced by our own cells; both the dose and the quality of the proteins produced are unknown. We also ignore the distribution of the lipid nanoparticles carrying this mRNA in our body. We consider that the “conscientious objection” raised by the above considerations is a reason enough to refuse mRNA vaccines or similar technologies as a preventive treatment against COVID-19.
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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 | 0.036 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".