The COVID-19 Pandemic: On the Everyday Mechanisms of Social Murder
Bibliographic record
Abstract
The goal of this commentary is to explore and reflect upon some of the everyday normalized mechanisms of social murder operating in the Covid-19 pandemic. Although social murder is activated in a complex and hidden process, it is nonetheless put in place by actual policymakers in the course of their actual everyday lives. Drawing on Engels’ original writings about social murder, and the work of contemporary authors such as Chernomas and Hudson, Birn, Grover, and Hodkinson, I explore the relentlessness of social murder – a deeply entrenched historical repetition of lethal, public policy-induced disease and illness. Using the cycle of oppression (stereotype, prejudice, discrimination, oppression) I illustrate in more granular detail how some of these mechanisms play themselves out in the social murder of the COVID-19 pandemic. Although oppression and social murder are somewhat abstract concepts, they are (re)envisioned and (re)enacted in the material world we live in, by actual people, especially those who operate in the public policy realm. I conclude with Scambler’s greedy bastards hypothesis (GBH), underscoring that the perpetrators are known, as are the policy-based solutions.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".