Economies of Scale in the Canadian Food Processing Industry
Bibliographic record
Abstract
Cost functions for three Canadian manufacturing agri-food sectors (meat, bakery and dairy) are estimated using provincial data from 1990 to 1999. A translog functional form is used and the concavity property is imposed locally. The Morishima substitution elasticities and returns to scale elasticities are computed for different provinces. Inference is carried out using asymptotic theory as well as bootstrap methods. In particular, the ability of the double bootstrap to provide refinements in inference is investigated. The evidence suggests that there are significant substitution possibilities between the agricultural input and other production factors in the meat and bakery sectors. Scale elasticity parameters indicate that increasing returns to scale are present in small bakery industries. While point estimates suggest that increasing returns to scale exist at the industry level in the meat sector, statistical inference cannot rule the existence of decreasing returns to scale. To account for supply management in the dairy sector, separability between raw milk and the other inputs was introduced. There exists evidence of increasing returns to scale at the industry level in the dairy industries of Alberta and New Brunswick. The scale elasticity for the two largest provinces (Ontario and Quebec) is greater than one, but inference does not reject the null hypothesis of increasing returns to scale.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".