Bibliographic record
Abstract
In recent years, some economists and journalists have argued that since only 7% of Canadian forests are under private ownership, Canadian public forests should be sold to private companies. In this paper, I examine and analyze global forest ownership and recent trends in the change in forest land ownership. In Canada, 26.5 million ha of forest land are under private ownership, while the area of forest land (of each country) of more than 200 countries, including Sweden, Finland, Germany, France, Japan, and New Zealand, is less than the area of Canada’s private forest land. Similarly, the forest industry in Canada owns more forest land available for wood supply than the forest industry in any other developed country except the USA and Sweden. There is no direct relationship between private forest ownership and the economic performance of forest industry in a country. I examine 3 cases of change in forest land ownership: Timber Investment Management Organizations and Real Estate Investment Trusts in the USA, restitution of forest land in economies in transition, and sale of plantations in Chile. None of the cases provide economic evidence in support of sale of Canadian public forests. I conclude that the sale of the Crown forest land will not only be environmentally, socially, and politically unacceptable, but will not be economically viable. Key words: Canada, economic performance, forest ownership, forest tenure, privatization, restitution of forest land, timber investment management organizations, wood supply
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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 | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".