Thick critiques, thin solutions: news media coverage of meatpacking plants in the COVID-19 pandemic
Bibliographic record
Abstract
The human labor and animal inputs required to manufacture meat products are kept physically and symbolically distanced from the consumer. Recently however, meatpacking plants received significant news media attention when they emerged as hotpots for COVID-19 — threatening workers’ health, requiring plants to slow production, and forcing farmers to euthanize livestock. In light of these disruptions, this research asks: how did news media frame the impact of COVID-19 on the meat industry, and to what extent is a process of defetishization observed? Examining a sample of 230 news articles from coverage of US meatpacking plants and COVID-19 in 2020, I find that news media largely attributes the cause for the spread of COVID-19 in meatpacking plants to the history of exploitative working conditions and business practices of the meat industry. By contrast, the solutions offered to address these problems aim at alleviating the immediate obstacles posed by the pandemic and returning to, rather than challenging, the status quo. These short-run solutions for complex issues demonstrate the constraints in imagining alternatives to a problem rooted in capitalism. Furthermore, my analysis shows that animals are only made visible in the production process when their bodies become a waste product.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".