Bibliographic record
Abstract
Some resource economists and policy-makers believe that market mechanisms in general and timber pricing through auctions specifically are the only solutions for forest management in Canada. In this paper, simple economic concepts of market, economic efficiency, and social optimality are discussed, and the specific features of forest resources and sustainable forest management and their implications for optimal resource allocation through the market are highlighted. Economic theory behind competitive timber pricing in two geographical regions is presented to demonstrate that in a competitive setting, the prices of timber need not be the same in the two regions. Timber pricing mechanisms used by different countries are summarized, and auctions, their limitations, and some important outcomes of timber auctions by the United States Forest Service are discussed. Market performances of residual value and auction-based timber pricing are compared. On the basis of these discussions, it is inferred that sustainable forest management cannot be achieved either by the market or by government-controlled mechanisms only. An optimal-mix of the market and government-controlled mechanisms is the only answer to achieve sustainable forest management. Key words: auction, Canada, economic efficiency, market, residual value, social optimality, sustainable forest management, timber pricing
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".