Comparative analysis of the production technologies of logging, sawmill, pulp and paper, and veneer and plywood industries in Ontario
Bibliographic record
Abstract
Technological growth in production and efficient utilization of input factors are the two biggest contributors to total factor productivity (TFP). TFP of the four major forest industries (logging, pulp and paper, sawmill, and veneer and plywood industries) of Ontario are compared by analyzing their production structures using duality theory in production and costs. The study uses annual data of output and four inputs — labour, capital, energy and materials — from 1967 to 2003. Different restrictions on the translog cost function are applied to each industry to determine the cost function that best describes each industry’s technology, which is further used to estimate Morishima elasticities of substitution, own-price and cross-price elasticities, rate of technological change, and TFP. The production structure of sawmill and veneer and plywood industries is found to be linear homogeneous and homothetic, and that of logging and pulp and paper industries is non-homothetic. Further, Hicks neutral technological change for all four industries is rejected, indicating that the production structure in all four industries is biased in favour of certain inputs and against others. This suggests that policies that improve the efficiency of each industry should focus on input-saving factors of that industry, thereby improving its competitive position.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".