Economic explanation for privatization of forests and forestland: Canada and the United States
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY This paper analyses the differences between forestland tenure systems in Canada and the US. Evolution of the two systems is primarily explained by variation of scarcity and land productivity. In early colonial times, Canada's economy was tightly linked to the fur trade with Indian people, while New England's economy was based more on agriculture with more intensive land use. In Canada, the focus was on the rights to timber from natural forests due to its abundant natural forests, poor road access and low productivity for farming, whereas in the US, the focus was on not just the forests but also the land as land was valuable for farming. While we emphasize the differences in scarcity in timber and timberland as the main cause of the disparity of the forestland tenure systems between the two countries, some legal and political factors are also explored.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it