Vulnerabilities and Benefits of Mega-Scale Agri-Food Processing Facilities in Canada
Bibliographic record
Abstract
This paper explores the vulnerabilities associated with mega-scale agri-food processing facilities in the wake of Canada’s COVID-19 outbreak and associated disruptions to the food supply chain and food security as the result of disease outbreak-induced plant closures. The current system of mega-scale facilities has come about as a result of powerful economic incentives associated with economies of size and scale, which allow larger-scale facilities to spread fixed costs across more units of output, leading to lower per-unit costs, greater levels of profitability, and, in the case of the agri-food sector, lower prices and enhanced food security. Governmental and regulatory policies such as tax incentives for investment in large plants (to bring jobs into communities) and allowing the anti-competitive effects of larger/fewer plants to be a secondary consideration to efficiencies have also contributed to the phenomenon of large plant size. Unfortunately, however, increased plant size also requires more workers which can lead to increased risk of disease spread. Moreover, with fewer and larger plants, if a disease like COVID-19 is detected and a plant idled or shut down as a result, the impact of any individual plant being closed upon the food supply and food security is much greater. Three potential strategic policy options to address this are explored. The first is the introduction of a series of smaller, regional facilities which would greater match commodity availability to processing capacity. For example, Canada’s cattle slaughter capacity does not closely resemble the location of its cattle herd. A second possible option would be the encouragement of alternative ownership structures such as the 1990s-era “New Generation Co-operative” which would be farmer-owned and geographically dispersed. The third (and most likely) option is the introduction of greater levels of mechanization, which would require fewer workers, and increased intra-plant distancing and safety measures for those workers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".