Capitalism, the Left, and Nature as it Is: A Second Reply to Victor Wallis on COVID
Bibliographic record
Abstract
Wallis' reply to my earlier article continues to not understand the poor quality of the research that criticizes COVID vaccines. These articles accept totally inadequate data as if they were valid, and fail to compare the occasional harms the vaccines create (and which have been widely reported by good researchers) with the great benefits they bestow to far more people. He continues to ignore Long COVID, which devastates the lives of millions of people, including children and youth, and the fact that the vaccines are partially protective against Long COVID. He continues not to define what he means by the “Left” while apparently not knowing about much of what left activists and researchers have been doing about the state of healthcare generally and about COVID prevention and care.
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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.057 | 0.087 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".