Worked to the Bone: COVID-19, the Agri-Food Labour Force, and the Need for More Compassionate Post-Pandemic Food Systems (preprint)
Bibliographic record
Abstract
The coronavirus pandemic has rendered visible the previously invisible labour that gets our food from farm to fork for minimal pay and at great personal risk to workers’ health. From grocery clerks working on the front lines without protective equipment, to truckers denied entry to restrooms, to temporary foreign workers forced to sign liability release waivers, to disease transmission at meat processing facilities, the virus is revealing the frailties and the inequities of our food system. Although the coronavirus pandemic is unprecedented, the ways the global food supply chain has responded to the crisis were, in fact, predictable. For years, scientists and food policy experts have been warning that our food system was broken, and that policies geared towards efficiency and cheap food were exploitative of the agri-food labour force, the animals we raise and slaughter for food, and the ecosystems we inhabit. This chapter focuses on the impact of COVID-19 on labour, with particular emphasis on the meat processing industry. It also seeks to illustrate the interconnectedness of all actors across the supply chain and the need for greater compassion as we rebuild post-pandemic food systems.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".