Issues Related with the Introduction of a Planning Framework Founded on the Conservation of Recreational Experience into Natural Park Planning in Japan
Bibliographic record
Abstract
In order to manage the probelms induced by increasing public demands for outdoor recreation and diversification of recreational use in national parks, this study investigated the validity and the subjects for introducing key points of the planning frameworks developed in U.S.A. and Canada for coping with these problems into the planning of national parks in Japan. Research papers concerning about park planning process founded upon the framework of “Recreational Opportunity Spectrum” were studied to discuss key points in detail. As a result, there was a series of developed system to conserve natural resources and diversity of opportunities of recreational experience. Among the important elements presented were the followings: specific statements of park purpose; zoning system based on the characteristics of settings; identification of indicator reflecting conditions of each zone, standards specifying a limit of acceptable change; logical linkage between standards and park purpose; monitoring the conditions; countermeasure to unacceptable change of the conditions; and feedback of consequences of management action to revise the planning. Based on the result, we should survey to make inventories of recreational opportunities for zoning system and to identify indicators and standards of acceptable change of each zone.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".