Capacity Utilization and Productivity Analysis in the Canadian Food Manufacturing Industry
Why this work is in the frame
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Bibliographic record
Abstract
Food processing is Canada’s largest manufacturing employer, accounting for 236,000 jobs and the second largest manufacturing industry overall by revenue. However, the industry has recently experienced a considerable number of plant restructurings and a diminishing national trade surplus in processed food. The purpose of this study is to measure capacity utilization and multifactor productivity in order to examine the contribution of capacity utilization to change in productivity in the Canadian food manufacturing industry. I use data envelopment analysis and the Malmquist productivity index to measure capacity utilization and multifactor productivity in food manufacturing industry over the period 1990-2012 at provincial level. The results show every province (except Newfoundland) experienced a slowdown in multifactor productivity growth since 2000, the extent of which varies considerably by province. Capacity under-utilization is one important reason for Atlantic and Prairie Provinces’ productivity growth slowdown.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it