Production Structure, Technological Change and Scale Economies in the Saw and Planing Mills Industry in New Brunswick, Canada
Bibliographic record
Abstract
The translog cost function approach is employed to characterize the production structure and to estimate the rate of technical change and technical bias in the saw and planing mills industry (SPM) in the New Brunswick Province. The findings are that the production structure of the saw and planing mills in Canada is neither homothetic nor homogenous implying potential scale induced distortion in the input mix. Morishma elasticity of substitution estimates show that in the existing technology of the saw and planing mills in New Brunswick, labor can more easily be substituted by capital than capital by labor. Moreover, the amount of round wood that is required to complement labor is higher than that required to complement energy and capital, which indicates that a labor intensive technology choice in the SPM industry is more round wood consuming than the capital and energy intensive technologies. These results coupled with the increasingly stringent environmental regulations indicate that the relative use of labor compared to other inputs is likely to decline in the saw and planing mills industry. Hence, in view of their cost minimizing behavior, the saw and planing mills in New Brunswick will sooner or latter start to replace labor with energy or capital. The saw and planing mills in New Brunswick exhibited fairly high economies of scale during the period 1965-1995, but the rate of technical change has been found to be negative.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".