MARKET DYNAMICS ASSOCIATED WITH A BEEFPACKING PLANT CLOSING AND A PORKPACKING PLANT OPENING
Bibliographic record
Abstract
Previous research has estimated price effects of meat packing plant closings and openings. However, none have been done for plants opening or closing during the last 20 years ago when concentration in meatpacking increased rapidly. Plant openings and closings affect industry slaughtering capacity. Many analysts contribute the lack of processing capacity to handle the large supply of hogs in 1998 a major factor why spot market hog prices plummeted to unprecedented lows. Just eight months after the capacity constraint in slaughter hogs, Maple Leaf Foods opened a hog processing plant in Brandon, Manitoba. A second but opposite event occurred in the beef industry in an area of concentrated cattle feeding and meatpacking. On Christmas day, 2000, the ConAgra fed cattle processing plant was damaged by fire in Garden City, Kansas. The objective of this research is to determine the market effects of a plant opening in the porkpacking industry and a plant closing in the beef packing industry. Regression models were estimated to compare reported weekly average prices in the market where the plant opened or closed with comparable prices for benchmark markets before and after the plant opening or closing. Regression models followed previous research but explained relatively little of the variation in price ratios between the affected market area and comparison markets. Small price effects were found in some cases but with little consistency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".