Review of <i> Kill and Chill: Restructuring Canada's Beef Commodity Chain</i> by Ian MacLachlan
Bibliographic record
Abstract
Most North Americans are meat eaters but few care to ask where their meat comes from or how it is produced. Kill and Chill answers those questions from a Canadian perspective by chronicling the development of Canada's beef industry during the twentieth century. Canadian beef processing shares many similarities with its US counterpart. At the beginning of the 1900s cattle were raised on the Plains and Prairies and shipped eastwards in railcars to be sold in stockyards, then slaughtered in multi-species packinghouses. Chicago became the prototypical meatpacking town with its sprawling stockyards and packinghouse district, a pattern later emulated in Toronto. Packinghouse working conditions described in Upton Sinclair's 1906 The Jungle led US and Canadian authorities to introduce federal meat inspection, while US unions fought to improve workers' pay and conditions on both sides of the border. By the 1970s, meatpacking wages exceeded national averages for manufacturing employees in both countries. Since then cost-cutting innovations-pioneered, in many cases, by US beef processors-have led to a restructuring. Old urban plants have closed, large-slaughter-capacity, single-species plants have opened in small towns on the Plains and Prairies, and industry wages have fallen.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".