Bibliographic record
Abstract
'This text seeks to provide an introduction to issues of land use and the economic tools that are used to resolve land-use conflicts. In particular, tools of economic analysis are used to address allocation of land among alternative uses in such a way that the welfare of society is enhanced. Thus, the focus is on what is best for society and not what is best for an individual, a particular group of individuals, or a particular constituency. What this text seeks to provide is a balanced and just approach to decision-making concerning allocation of land.' -- from the Introduction Land Resource Economics and Sustainable Development has already been tested, in a slightly different format, on over 400 students in a number of upper-level undergraduate and lower-level graduate courses. It presents a pragmatic approach to the issues of land use and sustainable development, and breaks away from the narrow focus of most economics texts on resources as it takes into account current political and ecological concerns while at the same time providing readers with the essential economic tools for a rational discussion of land use conflicts. Land Resource Economics and Sustainable Development addresses a wide range of issues not covered in other economics texts. These include: soil erosion; wetlands preservation; global climatic change; urban/rural conflict; urban land use; range management; forest management; and public land management. The broad scope and practical perspective make Land Resource Economics and Sustainable Development useful to students, interdisciplinary researchers, and professional economists and managers working in the fields of economic development, the environment, agriculture, and forestry. Both U.S. and Canadian data are used throughout the text to illustrate the issues discussed in the book.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".