Bibliographic record
Abstract
These slides are intended for use by instructors who have adopted Forest Economics for use in their course. About the book: Forestry cannot be isolated from the forces that drive economic activity. While forestry involves using land, labour, and capital to produce goods and services from forests, economics helps in understanding how this can be done in ways that will best meet the needs of society. Therefore, a firm grounding in economics is integral to sound forestry policies and practices. This book, a major revision and expansion of Peter H. Pearse’s 1990 classic, provides this grounding. Updated and enhanced with advanced empirical presentation of materials, it covers the basic economic principles and concepts and their application to modern forest management and policy issues. Topics range from valuation of forest investments and unpriced forest goods and services to product markets, government intervention, property rights, taxation, and global issues of forests and the environment. Each chapter contains review questions and suggestions for further reading. Forest Economics draws on the strengths of two of the field’s leading practitioners who have more than fifty years of combined experience in teaching undergraduate and graduate courses in forest economics in the United States and Canada. Its comprehensive and systematic analysis of forest issues makes it an indispensable resource for students and practitioners of forest management, natural resource conservation, and environmental studies. For more information on the book, please visit UBC Press.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.424 | 0.247 |
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".