A Detailed Analysis of the Productivity Performance of the Canadian Food Manufacturing Subsector
Bibliographic record
Abstract
This report analyzes labour productivity, multifactor productivity and input trends in Canadian food manufacturing since 1961, with a focus on the entire time period and developments since 2000. It is found that the subsector experienced labour productivity growth stronger than the business sector over both the long and short term, but has outperformed manufacturing only in the more recent period. Labour productivity growth is decomposed into capital intensity and multifactor productivity growth, which are found to have contributed to growth almost equally, and labour composition growth accounted for less than 15 per cent over the 1961-2007 period. Underlying drivers of growth are identified and trends in technology, capacity utilization, human capital, economies of scale, machinery and equipment, international trade, and regulation are explored. Policy implications for fostering labour productivity growth based on the drivers are outlined. Finally, a conclusion summarizes the key findings of the paper.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".