Exploring causal linkages between sustainable forest ecosystem management and technological progress in Canadian logging industries
Bibliographic record
Abstract
Logging poses socioeconomic, ecological, and environmental dilemmas. On the one hand, it plays a significant role in sustaining the forest sector’s contributions to the national economy. On the other hand, however, logging operations are major causes of ecological and environmental damages. It was hypothesized that if timely investments in various silvicultural operations were made to restore ecological integrity disturbed by logging operation, if the guiding principles of sustainable forest ecosystem management were strictly upheld, and if public and private investments in research and development were made, with a view to realize technological progress in the forest sector, then logging operations would be technically and economically efficient, firms in each regional industry would have comparative cost advantages in the marketplace, and the adverse effects of logging operations on ecological integrity would be socially, economically, and environmentally tolerable, all reflected through total factor productivity (TFP) growth. Two complementary methodologies were applied to test this hypothesis. First, the guiding principles of sustainable forest ecosystem management were synthesized to establish the conditions and the principles that logging firms must uphold to be stewards of ecological integrity. Second, TFP growth was measured and analyzed, using a nonparametric model. Sluggish but upward trends in TFP growth appeared to validate the hypothesis. Implications of the study for policy making and the benefits that society derives from TFP growth are highlighted.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".