Benefit-cost analysis as a thought process applied to alternative uses of recreational resources in national parks, with special reference to Banff National Park, Canada, and the Peak District National Park, England.
Bibliographic record
Abstract
The intention of this thesis is to use the tool of Benefit-Cost analysis in the examination of differing approaches to land use in National Parks. The demand for all types of outdoor recreation is increasing rapidly, and the land resources held for recreation purposes at present are inadequate to meet this demand. National Parks are experiencing a share of this demand, and increasing use of the present resources is bringing problems to the National Parks. In most countries the resources of National Parks are used almost exclusively for recreation and its associated services, but in other cases the land resources in the National Parks support commercial uses as well. These two approaches to land use in National Parks are examined by means of case studies from the Canadian and British National Park systems. The facilities for recreation and the activities undertaken in Banff National Park, Alberta, and the Peak District National Park, England are described, and the problems that arise from the use of these National Park areas, both for recreation and other purposes, are outlined. The Benefit-Cost analysis process is used in the examination of alternative approaches to land use in National Parks. It is suggested that if the land resources in the Parks are to be used most effectively and efficiently, then a system of priorities of use should be established. Benefit-Cost analysis techniques are one method of arriving at such a system in a rational manner. This analytical approach can be used to evaluate between different land use schemes, e.g. between multiple-use and exclusive use as well as between different projects competing for the same resources. The analytical method is applied to two land use schemes in the Canadian National Park context. Without extensive survey, accurate figures and measurements of the exact costs and benefits involved in the two land use schemes cannot be obtained, so that in this instance Benefit-Cost analysis is used as a thought process only and no ratio is arrived at. However, Benefit-Cost analysis provides a logical method of approach to the discussion of alternative developments in National Parks.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".