The Left is Not Immune: Some Thoughts on COVID-Related Lab Leak and Vaccine Fixations
Bibliographic record
Abstract
Some on the Left in the United States (US) and other parts of the world have argued that the COVID-19 pandemic originated in a lab leak and/or that the vaccines for COVID-19 have done more harm than good. Criticisms have been aimed at the rest of the Left both for uncritically accepting mainstream views on COVID and for not contesting the censorship which (they claim) dissenting viewpoints have encountered. They see these stances as having seriously weakened the Left, at least in the US. Their contention that the lab leak hypothesis is important puts more of a focus on finding “perpetrators” rather than changing the overall capitalist system. In this House Organ, I present evidence that global capitalism is making zoonotic leaps of infectious agents from animals to humans—and thus pandemics—become more likely and more dangerous, and that COVID-19 vaccines have reduced illness and saved lives. I also challenge the claim that dissenting viewpoints have been censored. When sections of the Left argue against vaccination and insist on the importance of lab leaks as an issue, this can make it more likely that health advocates will engage in single-issue politics and avoid socialism, anarchism, and other general left political perspectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.016 |
| 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".