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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".