The Wealth of Forests: Markets, Regulation and Sustainable Forestry - eScholarship
Bibliographic record
Abstract
Review: The Wealth of Forests: Markets, Regulation and Sustainable Forestry By Chris Tollefson (Ed.) Reviewed by Natalie D. Reaves Rowan University, USA Chris Tollefson (Ed.). The Wealth of Forests: Markets, Regulation and Sustainable Forestry. Vancouver, Canada:University of British Columbia Press, 1999. 396 pp. ISBN 0-7748-0682-6 (hardcover); 0-7748-0683-4 (paper). US$85.00 hardcover; US$35.95 paperback. Acid-free paper. How does one balance the needs of industrial forestry versus the need for sustainable forestry in British Columbia, Canada? Which forest management policies are superior: market oriented or regulatory based ones? Exactly what policy instruments will encourage sustainable forestry with the needs of so many competing interests in British Columbia? These were the essential questions being addressed at a workshop held at the University of Victoria's Law School, British Columbia, Canada, in 1995. The Wealth of Forests is a collection of 15 papers from this conference written primarily by academics and activists in law, economics, and environmental science. Tollefson brings together an impressive group of scholars whose expertise is the greatest strength of this text. This is a valuable book and the editor covers a lot of territory and issues in detail. This is unfortunate because there are many fine sub-texts presented in the book. In any event, I will stick to a few general observations and let interested readers explore the book in detail. There are five sections in this book. Section I defines sustainable forestry. Section II, the strongest section of the book, offers a vigorous debate between four papers on the suitability of markets as tools for achieving sustainable forestry. Two papers are pro- market oriented, and two are anti-market in perspective. Section III examines the lessons learned from forest management in British Columbia. It is clear that sustainable forestry has failed in British Columbia. One contributor assesses the blame on incorrect timber pricing which influences the pace at which the forests are harvested. Another contributor attributes blame to an Allowable Annual Cut Rate that is too high. Section IV deals with the myriad of legal issues inherent in such a debate. This section is quite legalistic and one paper questions whether government should compensate land-owners when their private property is devalued by environmental regulations. This book draws heavily from law and economic theory, however, it is not intended for lawyers and economists. The book's audience appears to be
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".