Attitudes Towards Alternative Management Policies For Public Recreation Lands Abstract:
Bibliographic record
Abstract
Public recreation land management agencies have been searching for ways to increase revenue. User fees as implemented by the Fee Demonstration Program have received the most attention. Corporate sponsorships and private donations have also been implemented and other options, such as partial privatization, closure of some areas, and different forms of public-private partnerships have been debated. The present paper reports results from a 2002 mail survey of randomly selected Idaho and New Hampshire households, designed to elicit public attitudes about a wide variety of management policies for public (federal/state) recreation lands. The most socially acceptable forms for raising revenue were donation boxes (61.1%), corporate sponsorships for visitor centers (61.8%) and education facilities (61.1%), and adopt-a-site contracts (81.0%). Both increased taxes and user fees faced greater opposition (about 49 % of respondents) than support (39%). In addition, user fees have resulted in (reported) changes in behavior among a quarter of the respondents, and may have stronger negative impact on lowincome households and on the users of public recreation lands. This study is relevant to the current debate in Congress about the future of the Fee Demonstration Program. Our findings can also be used by policy makers interested in raising
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".