Bibliographic record
Abstract
Abstract The chapter examines the origins of the tenures in American and Canadian public forests. They began with the licenses on English Crown forests, the medieval monarch's personal property. On such forests naval timber reserves were created, first in England then overseas. Desirable trees were marked with a broad arrow. After the revolution both the US and Canadian governments succeeded to huge acreages covered with trees. The new US federal government did not develop a specific timber-land tenure or sale procedure. The chapter offers the explanation of high classification costs and enforcement costs. Also the Canadian provinces were prevented by high land classification costs from offering timber lands for outright sale. Instead they offered timber-cutting licenses, and later they assigned forests to particular firms as incentives for investing in sawmills. In the 1940s Canadian provinces went one step further and offered to combine a mill's owned or licensed forest with dedicated government forest, thus producing large managed sustained-yield ‘agreement’ units. The US had already created managed National Forests, some closely integrated with neighbouring timber-using plants and communities, but tended by government. Apart from their agreement units, the Canadian provinces now offer short-duration licenses or ‘timber sales’ to commercial loggers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".