Parametric Characterization of an Industrial Production Technology: The Canadian Sawmilling Industry’s Case
Bibliographic record
Abstract
The goal of this study was to analyze a long-run technological progress in the Canadian sawmilling industry. Technological progress was considered as any kind of shift in the production technology estimated by total factor productivity growth (TFPG) and other parameters that complemented it. Out of six econometric models that were tested for efficacy in describing the technology, an unrestricted translog functional-form of a long-run total cost function described the technology sufficiently. The industry’s TFPG averaged 2.3% per year over the study period. Factor substitution elasticities implied that it was easy for the industry to substitute labor for capital and energy. The industry recorded increasing returns to scale and economies of scale; and technological progress was biased toward capital-using, energy-saving, and Hicks-neutral for labor and material. The multiple benefits that society derives from TFPG include: being one of the engines of economic growth, mitigation of natural capital depletion, minimization of wasteful-use of factors of production, mitigation of the adverse effects of inflation, boosting economic savings, freeing input factors to be reallocated to production of other goods and services, improvements in industrial competitiveness in the marketplace, and revealing possibilities to raise wage rates. Implications of the findings for industrial policymaking are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".