Bibliographic record
Abstract
This book evolved out of my own research in agriculture and forestry and out of courses in land economics that I taught at the Universities of Saskatchewan and British Columbia over a period of ten years.In general, the students in land economics at these schools differed in their academic backgrounds and in their living environments; yet, the tools that are used to examine land-use conflicts are similar.Students in British Columbia tend to be more sensitive to land use in an urban setting (e.g., the need for open space and denser settlement), but they are also interested in water and air quality, biodiversity and old-growth forests, scenic amenities, and so on.While Saskatchewan students are more interested in land use issues related to agriculture, they are obviously not insensitive to a beautiful landscape, opportunities for recreational activities, or the need for wildlife habitat.Therefore, the methodology presented in this book is useful to both types of students, since it is appropriate for analyzing many problems related to multiple land use and land-use conflicts.The tools of economics, as employed in this text, provide a useful means for talking about real world problems.In particular, they provide a functional starting point for rationally discussing land-use conflicts.Certainly, economics does not lend support to any one viewpoint in matters dealing with land use, environment, and sustainable development.Economics provides a perspective on multiple land use and land-use conflicts that is helpful in resolving the debate between environmentalists and developers, assuming that a compromise is desired.As opposed to the rhetoric and populist arguments one usually finds in the media concerning land-use issues, economics provides a rational and focused approach to solving problems.Surprisingly to some, it can lend support to the arguments of environmentalists in many situ-
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.522 | 0.346 |
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".