Bibliographic record
Abstract
This textbook is a thorough revision and expansion of Introduction to Forestry Economics by Peter H. Pearse, published in 1990.It is written for undergraduate forestry students taking courses in forest economics and for graduate students with diverse academic backgrounds who are interested in forest management and policy.This book reflects the two authors' more than 50 years of combined experience in teaching undergraduate and graduate courses in forest economics in the United States and Canada.It differs from the earlier book in its additional and updated content and its more advanced, empirical presentation of materials.Yet the emphasis is still on basic economic concepts, principles, and constructs used to analyze key features of private and public forestry decision making.Forestry, as we see it, is the applied science of managing land and trees to advance social objectives, which may relate to the production of industrial timber, recreation, or a variety of other goods and services of value to people.Economics is concerned with choices about how resources are allocated and used to create things of value to people.Having begun careers as foresters and later turned to economics, we have found that the two areas of study converge and complement each other.Forestry involves using land, labour, and capital to produce goods and services from forests, while economics helps in understanding how this can be done in ways that will best meet the needs of people.Moreover, it is increasingly apparent that we cannot isolate forestry from the economic forces that drive other activities.The growing intensity and variety of demands on forests for recreational, aesthetic, and environmental benefits as well as for timber give rise to complicated
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.518 | 0.345 |
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".