Bibliographic record
Abstract
Forest economists have had a checkered history in showing their relevance to foresters in Canada. At the same time, foresters have sometimes seemed to ignore social considerations, identified by economists, in practices and policies. Although communication seems to be improving, there are still a number of divisive issues and associated viewpoints that inhibit the use of economics in forestry. In this paper, I investigate four hypotheses that could explain why forest economists and foresters in Canada have had such a hard time communicating. Hypothesis #1 is that economic concepts are irrelevant to forestry. Hypothesis #2 is that foresters are actually brilliant, but "dark side," economists that have structured systems to protect forests from economic forces. Hypothesis #3 is that foresters are "enlightened" economists that are catering to real social preferences that most economists do not understand. Hypothesis #4 is that forest economics may be relevant but is difficult within the contexts that it is practised. Analysis shows that while all hypotheses have elements of truth, the higher numbered hypotheses tend to be more supportable. Key words: relevance of forest economics, forest management, forest policy, social forestry, sustained yield
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".